In the publication [1] we implemented anonymization and synthetization techniques for a structured data set, which was collected during the HiGHmed Use Case Cardiology study [2]. We employed the data anonymization tool ARX [3] and the data synthetization framework ASyH [4] individually and in combination. We evaluated the utility and shortcomings of the different approaches by statistical analyses and privacy risk assessments. Data utility was assessed by computing two heart failure risk scores (Barcelona BioHF [5] and MAGGIC [6]) on the protected data sets. We observed only minimal deviations to scores from the original data set. Additionally, we performed a re-identification risk analysis and found only minor residual risks for common types of privacy threats. We could demonstrate that anonymization and synthetization methods protect privacy while retaining data utility for heart failure risk assessment. Both approaches and a combination thereof introduce only minimal deviations from the original data set over all features. While data synthesis techniques produce any number of new records, data anonymization techniques offer more formal privacy guarantees. Consequently, data synthesis on anonymized data further enhances privacy protection with little impacting data utility. We hereby share all generated data sets with the scientific community through a use and access agreement. [1] Johann TI, Otte K, Prasser F, Dieterich C: Anonymize or synthesize? Privacy-preserving methods for heart failure score analytics. Eur Heart J 2024;. doi://10.1093/ehjdh/ztae083 [2] Sommer KK, Amr A, Bavendiek, Beierle F, Brunecker P, Dathe H et al. Structured, harmonized, and interoperable integration of clinical routine data to compute heart failure risk scores. Life (Basel) 2022;12:749. [3] Prasser F, Eicher J, Spengler H, Bild R, Kuhn KA. Flexible data anonymization using ARX—current status and challenges ahead. Softw Pract Exper 2020;50:1277–1304. [4] Johann TI, Wilhelmi H. ASyH—anonymous synthesizer for health data, GitHub, 2023. Available at: https://github.com/dieterich-lab/ASyH. [5] Lupón J, de Antonio M, Vila J, Peñafiel J, Galán A, Zamora E, et al. Development of a novel heart failure risk tool: the Barcelona bio-heart failure risk calculator (BCN Bio-HF calculator). PLoS One 2014;9:e85466. [6] Pocock SJ, Ariti CA, McMurray JJV, Maggioni A, Køber L, Squire IB, et al. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J 2013;34:1404–1413.

Data De-Identification Platform Market Outlook

According to our latest research, the global Data De-Identification Platform market size reached USD 714.2 million in 2024, driven by the escalating need for data privacy and regulatory compliance across industries. The market is experiencing robust expansion, registering a CAGR of 18.7% from 2025 to 2033. By 2033, the market is forecasted to attain USD 3,276.9 million, reflecting the surging adoption of advanced data privacy solutions and the increasing volume of sensitive data handled by organizations worldwide. This remarkable growth trajectory is primarily fueled by stricter data protection laws, rising data breach incidents, and the imperative for organizations to leverage data analytics without compromising personal information.

The primary growth factor for the Data De-Identification Platform market is the intensification of global data privacy regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and other region-specific mandates. Organizations are increasingly mandated to ensure that personally identifiable information (PII) is adequately protected or anonymized before use in analytics, research, or sharing with third parties. This regulatory landscape compels enterprises to integrate sophisticated de-identification platforms into their data management workflows. Furthermore, as digital transformation accelerates across sectors, the volume and variety of data being collected and processed have grown exponentially, creating new challenges and opportunities for data privacy management. The need to balance data utility with privacy has made automated, scalable de-identification solutions a top priority for businesses aiming to remain compliant and competitive.

Another significant driver is the rising frequency and sophistication of data breaches and cyberattacks, which have heightened organizational awareness regarding the risks associated with storing and processing sensitive information. As enterprises increasingly migrate to cloud environments and adopt big data analytics, the attack surface expands, making robust data de-identification tools essential for mitigating exposure. These platforms enable organizations to anonymize or pseudonymize data, reducing the risk of re-identification even in the event of a breach. The growing adoption of artificial intelligence (AI) and machine learning (ML) further necessitates de-identification, as these technologies often require access to large datasets that must be stripped of personal identifiers to ensure ethical and legal compliance. This confluence of factors is propelling the demand for advanced, user-friendly, and highly configurable de-identification platforms.

Moreover, the proliferation of data-driven business models in sectors such as healthcare, BFSI, government, retail, and IT & telecom is amplifying the need for secure data sharing and collaboration. In healthcare, for instance, the use of patient data for research, clinical trials, and population health management demands rigorous de-identification to protect patient privacy while enabling valuable insights. Similarly, financial institutions and government agencies are leveraging data to enhance service delivery and operational efficiency, necessitating robust privacy controls. The increasing recognition of data as a strategic asset, coupled with the imperative to safeguard individual privacy, is fostering a culture of proactive data governance and driving investments in de-identification technologies.

The integration of Data De-identification AI is revolutionizing the way organizations handle sensitive information. By leveraging AI technologies, businesses can automate the process of identifying and anonymizing personal data, ensuring compliance with stringent privacy regulations. This approach not only enhances data security but also allows for more efficient data processing and analysis. AI-driven de-identification tools can dynamically adapt to new data patterns, providing organizations with a robust mechanism to protect personal information while still extracting valuable insights. As AI continues to evolve, its role in data de-identification is expected to become even more pivotal, driving innovation and setting new standards in data privacy management.

From a regional perspective, North America currently dominates the Data De-Identification P

De-identified Healthcare Data Market Outlook

According to our latest research, the global de-identified healthcare data market size reached USD 3.4 billion in 2024. The market is expanding at a robust CAGR of 15.2% and is forecasted to attain a value of USD 10.9 billion by 2033. This remarkable growth is primarily driven by the increasing demand for privacy-compliant data solutions that enable research, analytics, and innovation without compromising patient confidentiality. The adoption of stringent data privacy regulations and the rapid digitization of healthcare records are further fueling the market’s momentum.

One of the primary growth factors for the de-identified healthcare data market is the rising emphasis on patient privacy and security. The implementation of regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe has necessitated robust data de-identification processes. These regulations mandate the removal of personally identifiable information from healthcare datasets, making de-identified data a critical resource for organizations aiming to comply with legal requirements while still leveraging valuable insights for research and analytics. As healthcare organizations increasingly digitize patient records and data sharing becomes more prevalent, the demand for effective de-identification solutions continues to surge, driving market growth.

Another significant driver is the exponential growth in healthcare data volume, propelled by the widespread adoption of electronic health records (EHRs), wearable devices, and genomics. The sheer scale and diversity of healthcare data present both opportunities and challenges for healthcare stakeholders. De-identified data allows organizations to harness this vast information pool for applications such as clinical research, drug development, population health management, and artificial intelligence (AI) model training. Pharmaceutical and biotechnology companies, in particular, are leveraging de-identified datasets to accelerate drug discovery, optimize clinical trials, and identify patient cohorts, thereby shortening development timelines and reducing costs. This trend is expected to intensify as precision medicine and data-driven healthcare models gain traction globally.

Technological advancements are also playing a pivotal role in shaping the de-identified healthcare data market. The emergence of sophisticated de-identification software, advanced encryption algorithms, and secure data sharing platforms has enhanced the ability of organizations to anonymize and utilize healthcare data effectively. Artificial intelligence and machine learning tools are being increasingly deployed to automate the de-identification process, improving scalability and accuracy. Furthermore, partnerships between healthcare providers, technology vendors, and research institutions are fostering innovation and facilitating the adoption of best practices in data privacy. As these technologies continue to evolve, they are expected to lower operational barriers and expand the market’s reach across various healthcare segments.

From a regional perspective, North America holds the largest share of the de-identified healthcare data market, accounting for over 42% of global revenue in 2024. This dominance is attributed to the region’s advanced healthcare infrastructure, strong regulatory framework, and high adoption of digital health technologies. Europe follows closely, driven by stringent data privacy laws and robust investments in healthcare IT. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digital transformation, increasing healthcare expenditure, and growing awareness of data privacy issues. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as governments and healthcare organizations prioritize data-driven healthcare initiatives.

Component Analysis

The de-identified healthcare data market by component is segmented into software, services, and platforms. Software solutions form the backbone of the market, providing automated tools for data masking, anonymization, and encryption. These solutions are in high demand due to their ability to efficiently process vast volumes of healthcare data while ensuring compliance with regulatory standards. A

Healthcare Data Anonymization Services Market Outlook

According to our latest research, the global healthcare data anonymization services market size reached USD 1.42 billion in 2024, reflecting a robust expansion driven by increasing regulatory demands and heightened focus on patient privacy. The market is projected to grow at a CAGR of 15.8% from 2025 to 2033, with the total market value expected to reach USD 5.44 billion by 2033. This impressive growth trajectory is underpinned by the rising adoption of digital health solutions, stringent data protection laws, and the ongoing digitalization of healthcare records worldwide.

The primary growth factor fueling the healthcare data anonymization services market is the proliferation of electronic health records (EHRs) and the expanding use of big data analytics in healthcare. As healthcare providers and organizations increasingly leverage advanced analytics for improving patient outcomes, there is a corresponding surge in data generation. However, these vast datasets often contain sensitive patient information, making data anonymization essential to ensure compliance with regulations such as HIPAA, GDPR, and other regional privacy laws. The increasing frequency of data breaches and cyberattacks has further highlighted the importance of robust anonymization services, prompting healthcare organizations to prioritize investments in data privacy and security solutions. As a result, demand for both software and service-based anonymization solutions continues to rise, contributing significantly to market growth.

Another key driver for the healthcare data anonymization services market is the growing emphasis on research and clinical trials, which require the sharing and analysis of large volumes of patient data. Pharmaceutical and biotechnology companies, as well as research organizations, are increasingly collaborating across borders, necessitating the anonymization of datasets to protect patient identities and comply with international data protection standards. The adoption of cloud-based healthcare solutions has also facilitated the secure and efficient sharing of anonymized data, supporting advancements in personalized medicine and population health management. As organizations seek to balance innovation with compliance, the demand for advanced anonymization technologies that offer high accuracy and scalability is expected to accelerate further.

Technological advancements in artificial intelligence (AI) and machine learning (ML) are also shaping the future of the healthcare data anonymization services market. These technologies are enabling more sophisticated and automated anonymization processes, reducing the risk of re-identification while maintaining data utility for research and analytics. The integration of AI-driven tools into anonymization workflows is helping organizations streamline operations, minimize human error, and achieve greater compliance with evolving regulatory requirements. Additionally, the increasing availability of customizable and interoperable anonymization solutions is making it easier for healthcare organizations of all sizes to adopt and scale these services, thereby broadening the market’s reach and impact.

From a regional perspective, North America continues to dominate the healthcare data anonymization services market, accounting for the largest share in 2024. This leadership position is attributed to the presence of advanced healthcare infrastructure, widespread adoption of EHRs, and strict regulatory frameworks governing patient data privacy. Europe follows closely, driven by the enforcement of the General Data Protection Regulation (GDPR) and a strong culture of data protection. The Asia Pacific region is witnessing the fastest growth, propelled by increasing healthcare digitalization, government initiatives to modernize healthcare systems, and rising awareness of data privacy among patients and providers. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as healthcare organizations in these regions begin to prioritize data security and compliance.

De-Identification Software for Healthcare Data Market Outlook

According to our latest research, the global market size for De-Identification Software for Healthcare Data in 2024 stands at USD 468 million, with a robust compound annual growth rate (CAGR) of 20.1% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach an impressive USD 2,633 million, reflecting substantial momentum driven by increasing regulatory demands and the proliferation of digital health records. As per our latest research, the primary growth driver for this sector is the intensifying focus on patient privacy and security in healthcare data management, propelled by global data protection regulations and the expanding adoption of electronic health records (EHRs).

The growth trajectory of the De-Identification Software for Healthcare Data Market is significantly influenced by the evolving regulatory landscape governing patient information privacy. Stringent regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, the General Data Protection Regulation (GDPR) in Europe, and similar frameworks globally are compelling healthcare organizations to invest in advanced de-identification solutions. These regulations mandate the removal or masking of personally identifiable information (PII) from healthcare datasets before sharing, research, or analytics, to safeguard patient privacy. As healthcare data becomes increasingly digitized, the risk of data breaches and unauthorized access grows, making robust de-identification software not just a compliance tool but a critical component of risk management strategies for healthcare providers, payers, and researchers.

Another significant growth factor is the rising volume and complexity of healthcare data generated through diverse sources such as EHRs, wearables, genomic sequencing, and telemedicine platforms. The integration of artificial intelligence (AI) and machine learning (ML) technologies into de-identification software has enabled more sophisticated and automated data anonymization processes, reducing manual intervention and improving accuracy. This technological advancement allows for the secure sharing of large-scale clinical and genomic datasets, which is crucial for collaborative research, population health analytics, and the development of personalized medicine. As the demand for interoperability and data exchange across healthcare ecosystems intensifies, scalable and automated de-identification solutions are becoming indispensable.

The market is further propelled by the expanding use of healthcare data for secondary purposes such as clinical research, public health monitoring, and healthcare analytics. Pharmaceutical companies, research organizations, and health insurers increasingly require access to de-identified datasets to derive insights, improve patient outcomes, and streamline operations without compromising privacy. The growing trend of data monetization and the emergence of health data marketplaces are also fueling the adoption of de-identification software, as organizations seek to unlock the value of their data assets while adhering to ethical and legal standards. These factors collectively create a fertile environment for sustained market growth over the forecast period.

Regionally, North America continues to dominate the De-Identification Software for Healthcare Data Market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high adoption rate of EHRs, advanced healthcare IT infrastructure, and the presence of leading market players in the United States and Canada underpin this leadership. Europe’s market is bolstered by GDPR compliance requirements and growing investments in digital health innovation, while Asia Pacific is witnessing rapid growth due to increasing healthcare digitization and a rising awareness of data privacy. Latin America and the Middle East & Africa are gradually emerging as promising markets, driven by healthcare modernization initiatives and evolving regulatory frameworks.

Component Analysis

The Component segment of the De-Identification Software for Healthcare Data Market is broadly categorized into Software and Services. The software segment holds the lion’s share of the market, primarily due to the growing need for automated

K-Anonymity Tools for Public Datasets Market Outlook

According to our latest research, the global K-Anonymity Tools for Public Datasets market size reached USD 1.14 billion in 2024, reflecting the growing necessity for robust privacy solutions across industries. The market is experiencing a strong expansion, registering a CAGR of 18.7% from 2025 to 2033. By 2033, the market is anticipated to reach a value of USD 6.32 billion, driven by increasing regulatory pressures, growing data volumes, and heightened awareness of data privacy. This growth is underpinned by the widespread adoption of K-anonymity tools in sectors handling sensitive public datasets, where data de-identification and privacy preservation are paramount.

One of the primary growth factors fueling the K-Anonymity Tools for Public Datasets market is the global surge in data privacy regulations such as GDPR, CCPA, and HIPAA. Organizations are now compelled to implement advanced anonymization techniques to ensure compliance with these stringent policies. K-anonymity, which guarantees that individual data entries cannot be distinguished from at least k-1 others, has emerged as a preferred solution for public dataset anonymization. The proliferation of massive datasets in healthcare, government, and research sectors further amplifies the demand for scalable and efficient anonymization tools. As data breaches and privacy violations continue to make headlines, enterprises are proactively investing in K-anonymity tools to mitigate reputational and financial risks, thereby propelling market growth.

Technological advancements and the integration of artificial intelligence and machine learning with K-anonymity tools are also significant growth drivers. Modern K-anonymity solutions now offer automated risk assessment, real-time anonymization, and customizable privacy thresholds, making them more adaptable to diverse organizational needs. The rising adoption of cloud-based solutions has further democratized access to sophisticated privacy tools, enabling small and medium enterprises to leverage K-anonymity without substantial capital outlays. Additionally, the growing trend of data sharing for research and analytics—especially in healthcare and academia—necessitates robust anonymization to protect individual identities while preserving data utility. This evolution of capabilities and accessibility is expected to sustain the market's upward trajectory.

Another crucial factor is the increasing collaboration between public and private sectors in data-driven initiatives. Governments are opening public datasets for research, innovation, and policy-making, but such initiatives come with heightened privacy concerns. K-anonymity tools provide a practical solution for balancing transparency and privacy in open data programs. The market is also witnessing substantial investments from venture capitalists and technology giants, further accelerating innovation and adoption. The convergence of privacy technology with broader digital transformation initiatives ensures that K-anonymity tools remain at the forefront of enterprise data governance strategies. As organizations prioritize ethical data use and responsible AI, the relevance and demand for these tools are set to intensify.

Regionally, North America leads the K-Anonymity Tools for Public Datasets market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The dominance of North America can be attributed to robust regulatory frameworks, high technology adoption rates, and the presence of major market players. Europe’s growth is propelled by strict data protection laws and widespread digitalization across sectors. Asia Pacific is rapidly emerging as a high-growth region, driven by expanding IT infrastructure, increasing digital health initiatives, and rising awareness of data privacy. Latin America and Middle East & Africa are also showing promising growth, albeit from a smaller base, as governments and enterprises in these regions gradually adopt data privacy best practices.

De-Identification Solutions for Medical Images Market Outlook

According to our latest research, the global De-Identification Solutions for Medical Images market size was valued at USD 425.8 million in 2024, with a robust growth trajectory projected at a CAGR of 13.6% from 2025 to 2033. By the end of 2033, the market is anticipated to reach USD 1,314.7 million. This remarkable expansion is primarily fueled by the increasing adoption of advanced imaging technologies in healthcare, stringent regulatory mandates for patient data privacy, and the rising prevalence of medical imaging data in clinical research and diagnostics. As per our latest research, the market is witnessing a dynamic shift towards cloud-based and AI-powered de-identification solutions, enabling healthcare organizations to meet compliance requirements while fostering innovation in medical imaging analytics.

One of the foremost growth drivers for the De-Identification Solutions for Medical Images market is the exponential rise in digital healthcare data, particularly from radiology, pathology, and cardiology departments. The proliferation of high-resolution imaging modalities such as MRI, CT, and PET scans has resulted in massive data volumes that require secure handling and anonymization. Healthcare providers and research organizations are increasingly recognizing the importance of de-identification to protect patient privacy, comply with regulations such as HIPAA, GDPR, and local data protection laws, and enable the secondary use of medical images for research, AI training, and collaborative studies. This trend is further amplified by the growing integration of electronic health records (EHRs) with imaging systems, necessitating robust and scalable de-identification solutions to mitigate the risk of data breaches and unauthorized disclosures.

Another significant factor propelling market growth is the rapid advancement of artificial intelligence and machine learning algorithms in the field of medical imaging. AI-driven de-identification tools are now capable of automating the anonymization process with high accuracy, reducing manual intervention, and ensuring consistent compliance with regulatory standards. These solutions not only streamline workflow efficiency but also enhance data utility for research and innovation. The increasing adoption of cloud-based platforms is further supporting the deployment of scalable de-identification services, enabling healthcare organizations to process and share large datasets seamlessly while maintaining stringent data privacy controls. This technological evolution is also facilitating the participation of smaller healthcare facilities and research institutes in global data-sharing initiatives, thereby broadening the market base.

The surge in clinical trials, multi-center research collaborations, and the emergence of precision medicine are also contributing to the robust demand for de-identification solutions for medical images. Pharmaceutical companies, contract research organizations (CROs), and academic institutes are increasingly leveraging de-identified imaging datasets to accelerate drug discovery, validate diagnostic algorithms, and conduct population health studies. The emphasis on interoperability and data standardization across healthcare systems is driving the adoption of sophisticated de-identification tools that can support multiple imaging formats and workflows. Furthermore, the COVID-19 pandemic has underscored the importance of secure data sharing for public health research, further catalyzing investments in advanced de-identification technologies.

From a regional perspective, North America continues to dominate the De-Identification Solutions for Medical Images market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of a well-established healthcare infrastructure, stringent regulatory oversight, and a high concentration of leading market players are key factors supporting market leadership in North America. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization of healthcare, increasing investments in medical imaging, and rising awareness of data privacy. Europe remains a significant market owing to robust data protection regulations and a strong focus on research and innovation. Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by healthcare modernization initiatives and growing participation in global health research networks.

About

The following datasets were captured at a busy Belgian train station between 9pm and 10pm, it contains all 802.11 management frames that were captured. both datasets were captured with approximately 20 minutes between then.

Both datasets are represented by a pcap and CSV file. The CSV file contains the frame type, timestamps, signal strength, SSID and MAC addresses for every frame. In the pcap file, all generic 802.11 elements were removed for anonymization purposes.

Anonymization

All frames were anonymized by removing identifying information or renaming identifiers. Concretely, the following transformations were applied to both datasets:

• All MAC addresses were renamed (e.g. 00:00:00:00:00:01)
• All SSID's were renamed (e.g. NETWORK_1)
• All generec 802.11 elements were removed from the pcap

In the pcap file, anonymization actions could lead to "corrupted" frames because length tags do not correspond with the actual data. However, the file and its frames are still readable in packet analyzing tools such as Wireshark or Scapy.

The script which was used to anonymize is available in the dataset.

Data

Dataset contents

The two datasets are stored in the directories `1/` and `2/`. Each directory contains:

• `capture-X.pcap`: an anonymized version of the original capture
• `capture-X.csv`: content of each captured frame (timestamp, MAC address...) saved as a CSV file

`anonymization.py` is the script which was used to remove identifiers.

`README.md` contains the documentation about the datasets

License

Copyright 2022-2023 Benjamin Vermunicht, Beat Signer, Maxim Van de Wynckel, Vrije Universiteit Brussel

Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the “Dataset”), to deal in the Dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the Dataset is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions that make use of the Dataset.

THE DATASET IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.

De-Identification Software for Healthcare Data Market Outlook

According to our latest research, the global De-Identification Software for Healthcare Data market size reached USD 410 million in 2024, reflecting a robust surge in demand for data privacy and compliance solutions. The market is projected to expand at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 1,444 million by 2033. This significant growth is primarily driven by escalating regulatory requirements, increasing incidences of data breaches, and the proliferation of digital health data across healthcare systems worldwide.

One of the primary growth factors for the De-Identification Software for Healthcare Data market is the tightening of data privacy regulations such as HIPAA in the United States, GDPR in Europe, and similar frameworks in other regions. These legislations mandate stringent procedures for handling personally identifiable information (PII) and protected health information (PHI), compelling healthcare organizations to adopt advanced de-identification solutions. As healthcare providers, payers, and research entities increasingly digitize patient records, the risk of data exposure intensifies, making robust de-identification tools indispensable for compliance and risk mitigation. Furthermore, the growing awareness among healthcare professionals and administrators regarding the consequences of non-compliance, including hefty fines and reputational damage, is accelerating the adoption of these solutions.

Another critical driver is the exponential growth of healthcare data generated from electronic health records (EHRs), wearable devices, telemedicine platforms, and genomic studies. The sheer volume and complexity of this data necessitate sophisticated de-identification software capable of processing both structured and unstructured information. The demand is further amplified by the surge in collaborative research, clinical trials, and data sharing initiatives, which require the anonymization of patient data to protect privacy while enabling valuable insights. As artificial intelligence and machine learning applications become more prevalent in healthcare, the need for high-quality, de-identified datasets is also rising, fostering further market expansion.

Additionally, the rise in cyber threats and high-profile data breaches within the healthcare sector have underscored the urgent need for comprehensive data protection strategies. Healthcare organizations are increasingly prioritizing investments in de-identification software to safeguard sensitive patient information from unauthorized access and malicious actors. This trend is supported by the growing involvement of insurance companies and research organizations, which handle vast amounts of patient data and are equally vulnerable to breaches. The convergence of these factors is expected to sustain the momentum of the De-Identification Software for Healthcare Data market over the forecast period.

From a regional perspective, North America continues to dominate the market, accounting for the largest share in 2024, driven by robust healthcare infrastructure, early adoption of advanced technologies, and strict regulatory frameworks. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitization of healthcare systems, increasing investments in health IT, and rising awareness of data privacy. Europe, with its comprehensive data protection laws, also represents a significant market, while Latin America and the Middle East & Africa are gradually catching up as healthcare modernization accelerates in these regions. The global landscape is thus characterized by both mature and emerging markets, each contributing to the overall growth trajectory.

Data Loss Prevention in Healthcare is becoming increasingly crucial as the industry continues to digitize and expand its data management capabilities. With the rise of electronic health records, telemedicine, and wearable health devices, the volume of sensitive patient information being handled by healthcare organizations has skyrocketed. This surge in data has made the sector a prime target for cyberattacks, emphasizing the need for robust data loss prevention strategies. Healthcare providers are now investing in advanced technologies and protocols to protect patient data from unauthorized access and bre

Data De-identification AI Market Outlook

According to our latest research, the global Data De-identification AI market size reached USD 1.42 billion in 2024, reflecting a strong demand for advanced privacy technologies across industries. The market is expected to grow at a robust CAGR of 27.4% from 2025 to 2033, with the forecasted market size anticipated to reach USD 12.38 billion by 2033. This remarkable growth is primarily driven by stringent regulatory compliance requirements and the exponential rise in sensitive data generation worldwide, fueling the adoption of AI-powered de-identification solutions.

One of the key growth factors propelling the Data De-identification AI market is the intensifying global focus on data privacy and security. Regulatory frameworks such as the GDPR in Europe, CCPA in California, and similar data protection acts across Asia Pacific and Latin America are mandating organizations to implement robust data anonymization and de-identification practices. As the volume of personal and sensitive data continues to surge, especially in sectors like healthcare, BFSI, and government, enterprises are increasingly turning to AI-driven de-identification tools to ensure compliance while maintaining data utility for analytics and innovation. This regulatory pressure, combined with heightened consumer awareness about data privacy, is significantly accelerating market expansion.

Another major driver is the rapid digital transformation across industries, resulting in massive data collection and exchange. Organizations are leveraging big data analytics, machine learning, and cloud computing to derive actionable insights from vast datasets. However, this also raises the risk of data breaches and misuse of personally identifiable information (PII). AI-powered data de-identification solutions offer advanced capabilities such as automated masking, tokenization, and pseudonymization, enabling organizations to securely share and analyze sensitive information without compromising privacy. This capability is particularly crucial for sectors like healthcare and financial services, where data-driven innovation must be balanced with strict privacy requirements.

Furthermore, the proliferation of AI and machine learning applications is creating new opportunities and challenges in managing sensitive data. As organizations deploy AI models that require large-scale, real-world datasets, the need to de-identify data before use becomes paramount. AI-based de-identification tools not only expedite this process but also enhance accuracy and scalability, supporting the development of ethical and compliant AI systems. Additionally, the growing adoption of cloud-based solutions and the increasing integration of de-identification technologies into existing data management workflows are further boosting market growth. The convergence of these factors is expected to sustain the upward trajectory of the Data De-identification AI market throughout the forecast period.

Regionally, North America currently leads the market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of major technology providers, a mature regulatory environment, and high digital adoption rates. However, Asia Pacific is anticipated to witness the fastest growth over the next decade, fueled by rapid digitalization, expanding healthcare infrastructure, and increasing government initiatives to strengthen data privacy. Europe continues to be a strong market due to its rigorous GDPR compliance landscape, while Latin America and the Middle East & Africa are emerging as promising regions with growing investments in digital transformation and data security.

Component Analysis

The Data De-identification AI market by component is segmented into software and services, each playing a pivotal role in the overall ecosystem. The software segment currently dominates the market, driven by the increasing need for automated, scalable, and customizable data de-identification solutions. These software platforms are equipped with advanced features such as AI-based masking, encryption, and pseudonymization, enabling organizations to efficiently process large volumes of sensitive data in real-time. The integration of machine learning algorithms allows for context-aware de-identification, reducing the risk of re-identification while preserving data utility for analytics and machine learning

Clinical Data De-Identification Pipelines Market Outlook

According to our latest research, the global clinical data de-identification pipelines market size reached USD 680 million in 2024, with a robust growth trajectory driven by stringent data privacy regulations and the increasing adoption of digital health records. The market is expected to expand at a CAGR of 15.6% from 2025 to 2033, with the forecasted market size projected to reach USD 2.1 billion by 2033. This growth is primarily attributed to the rising emphasis on patient data security, the proliferation of healthcare data, and the need to facilitate compliant data sharing for research and analytics.

The rapid digitalization of healthcare systems worldwide has resulted in an unprecedented surge in electronic health records (EHRs), clinical trial data, and patient registries. As healthcare organizations increasingly leverage these vast datasets for research, analytics, and population health management, the risk of data breaches and unauthorized disclosures has escalated. This scenario has intensified the demand for robust clinical data de-identification pipelines, which ensure that personally identifiable information (PII) is systematically removed or masked before data is shared or analyzed. Regulatory frameworks such as HIPAA in the United States, GDPR in Europe, and similar mandates in other regions have made de-identification not just a best practice but a legal requirement, further propelling the adoption of advanced software and services in this market.

Another significant growth driver for the clinical data de-identification pipelines market is the expanding landscape of clinical research and precision medicine. Pharmaceutical and biotechnology companies, as well as academic and research institutes, are increasingly reliant on large-scale, multi-source datasets to accelerate drug discovery, understand disease mechanisms, and personalize treatment protocols. However, these research initiatives necessitate stringent privacy safeguards to maintain patient confidentiality while enabling meaningful data analysis. The integration of artificial intelligence (AI) and machine learning (ML) technologies into de-identification pipelines has enhanced the accuracy and efficiency of data anonymization processes, thereby supporting the dual objectives of compliance and research innovation.

Strategic partnerships and collaborations among healthcare providers, technology vendors, and research organizations have also played a pivotal role in shaping the clinical data de-identification pipelines market. Leading technology firms are investing in the development of scalable, interoperable solutions that can seamlessly integrate with existing healthcare IT infrastructure. Moreover, the emergence of cloud-based deployment models has made de-identification solutions more accessible to smaller healthcare entities and research organizations, democratizing access to advanced privacy tools. This trend is particularly pronounced in regions with rapidly evolving healthcare ecosystems, such as Asia Pacific and Latin America, where digital health initiatives are gaining momentum.

From a regional perspective, North America continues to dominate the clinical data de-identification pipelines market, accounting for the largest revenue share in 2024. This leadership is underpinned by the presence of a mature healthcare IT infrastructure, strong regulatory oversight, and significant investments in clinical research. Europe follows closely, benefiting from stringent data protection laws and a vibrant research community. Meanwhile, Asia Pacific is emerging as the fastest-growing market, fueled by large-scale government initiatives to digitize healthcare, rising awareness about patient privacy, and the increasing participation of regional players in global clinical research networks. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as healthcare modernization efforts gather pace.

Component Analysis

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Veterinary Data De-Identification Services Market Outlook

According to our latest research, the veterinary data de-identification services market size reached USD 145.8 million in 2024, reflecting a growing emphasis on data privacy and regulatory compliance in the veterinary sector. The market is poised for robust expansion, projected to attain USD 393.2 million by 2033, propelled by a CAGR of 11.7% from 2025 to 2033. This growth is primarily fueled by the increasing digitization of veterinary records, rising concerns over data security, and the integration of advanced technologies in veterinary healthcare management.

The surge in demand for veterinary data de-identification services is largely attributed to the exponential growth of digital data in the veterinary industry. As veterinary practices, research institutes, and pharmaceutical companies increasingly adopt electronic health records and data-driven approaches, the volume of sensitive animal health data has soared. This growth has necessitated robust data protection strategies to safeguard confidential information, especially as regulations similar to human healthcare data privacy, such as GDPR and HIPAA-like standards, are being extended to veterinary data. The need to anonymize and pseudonymize animal health data for research, clinical trials, and collaborative studies without compromising privacy is a significant market driver, pushing organizations to invest in specialized de-identification services.

Another key growth factor is the rising collaboration between veterinary clinics, research institutions, and pharmaceutical companies. These collaborations often require the sharing of large datasets to advance veterinary science, drug development, and clinical research. However, the sharing of identifiable data poses ethical and legal risks, elevating the importance of de-identification solutions that ensure compliance and foster trust among stakeholders. The increasing prevalence of zoonotic diseases and the global focus on One Health initiatives have further highlighted the need for secure and compliant data sharing, driving the uptake of de-identification services across the veterinary ecosystem.

Technological advancements are also reshaping the veterinary data de-identification services market. The integration of artificial intelligence, machine learning, and blockchain technologies has enhanced the efficacy and reliability of de-identification processes. These innovations enable more precise anonymization and encryption of veterinary data, reducing the risk of re-identification while maintaining data utility for research and analytics. Additionally, the growing awareness among veterinary professionals about the risks of data breaches and the potential legal consequences has led to increased investments in comprehensive data de-identification and security solutions, further propelling market growth.

From a regional perspective, North America continues to dominate the veterinary data de-identification services market, accounting for the largest revenue share in 2024. The region’s leadership is supported by stringent data privacy regulations, a high concentration of veterinary research institutions, and rapid adoption of digital health technologies. Europe follows closely, driven by strong regulatory frameworks and increasing investments in veterinary research. Asia Pacific is emerging as a high-growth region, with expanding veterinary healthcare infrastructure, rising pet ownership, and growing awareness of data privacy. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as digital transformation initiatives gain traction in these regions.

Service Type Analysis

The service type segment in the veterinary data de-identification services market encompasses anonymization, pseudonymization, data masking, encryption, and other specialized services. Anonymization remains the most widely adopted service, as it irreversibly removes personally identifiable information from veterinary datasets, ensuring compliance with stringent data privacy regulations. Veterinary clinics and research institutions favor anonymization for sharing data in multi-institutional studies and public health surveillance, as it allows for the safe aggregation and analysis of large datasets without risking the exposure of sensitive information. The growing complexity of veterinary data, including genomic and behavioral da

Data De-Identification for Omics Market Outlook

According to the latest research, the global Data De-Identification for Omics market size reached USD 1.28 billion in 2024, supported by a robust demand for privacy-preserving technologies in the biomedical and healthcare sectors. The market is expanding at a CAGR of 17.6% and is projected to attain a value of USD 5.85 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of omics technologies in clinical research and drug discovery, coupled with stringent data privacy regulations across regions.

One of the major growth factors propelling the Data De-Identification for Omics market is the exponential increase in the generation of omics data, particularly from genomics, proteomics, and metabolomics studies. As next-generation sequencing and high-throughput omics platforms become more affordable and widespread, vast amounts of sensitive biological data are being produced daily. This surge necessitates robust data de-identification solutions to protect patient privacy and comply with global regulatory frameworks such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and other data protection laws. The risk of re-identification from omics datasets, given their highly personal and unique nature, further underscores the need for advanced anonymization and pseudonymization tools, thereby fueling market demand.

Another significant driver is the integration of omics data in personalized medicine and precision healthcare initiatives. As healthcare providers and pharmaceutical companies increasingly leverage genomics and other omics data to tailor treatments and therapies, ensuring the privacy and security of this information becomes paramount. Data de-identification technologies enable organizations to share and analyze omics datasets without compromising individual identities, thereby accelerating collaborative research and innovation. Moreover, the growing trend of cross-border clinical trials and international research collaborations is amplifying the need for standardized, interoperable de-identification solutions that can operate seamlessly across jurisdictions, further catalyzing market expansion.

Technological advancements in artificial intelligence and machine learning are also transforming the Data De-Identification for Omics market. AI-powered de-identification platforms can automate the detection and masking of personal identifiers in complex omics datasets, significantly reducing manual effort and the risk of human error. These intelligent systems are capable of adapting to evolving data types and regulatory requirements, offering scalability and flexibility to research organizations and healthcare providers. Additionally, the increasing adoption of cloud-based solutions is facilitating secure, scalable, and cost-effective data de-identification workflows, making these technologies accessible to a broader range of end-users, from large pharmaceutical companies to small research institutes.

Regionally, North America continues to dominate the Data De-Identification for Omics market, accounting for the largest market share in 2024. This leadership is attributed to the presence of leading omics research institutions, robust healthcare infrastructure, and strict regulatory frameworks governing data privacy. Europe follows closely, driven by the implementation of GDPR and the region’s strong focus on biomedical research. The Asia Pacific region is witnessing the fastest growth, propelled by increasing investments in healthcare infrastructure, expanding genomics research, and rising awareness of data privacy. Latin America and the Middle East & Africa are also emerging as promising markets, supported by government initiatives to modernize healthcare systems and encourage biomedical innovation.

Component Analysis

The Component segment of the Data De-Identification for Omics market is bifurcated into software and servic

K-Anonymity Tools for Public Datasets Market Outlook

According to our latest research, the K-Anonymity Tools for Public Datasets market size was valued at $1.2 billion in 2024 and is projected to reach $4.6 billion by 2033, expanding at a robust CAGR of 16.4% during the forecast period of 2025–2033. The primary driver propelling this market’s global growth is the surging demand for advanced privacy-preserving technologies, especially as organizations worldwide increasingly share and analyze public datasets that contain sensitive or personally identifiable information. With rising incidents of data breaches and strict regulatory mandates such as GDPR and CCPA, organizations are compelled to adopt robust anonymization solutions to ensure compliance and maintain public trust. K-anonymity tools, which enable the release of datasets while minimizing individual re-identification risk, are becoming indispensable across sectors like healthcare, government, BFSI, and research, thereby fueling market expansion.

Regional Outlook

North America currently holds the largest share of the global K-Anonymity Tools for Public Datasets market, accounting for over 36% of total revenue in 2024. This dominance is attributed to the region’s mature data privacy ecosystem, early adoption of sophisticated data anonymization technologies, and the presence of leading market players. Stringent regulatory frameworks such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and robust investments in public sector data initiatives have catalyzed the integration of K-anonymity tools across government, healthcare, and BFSI sectors. Furthermore, the region benefits from a highly skilled workforce, advanced IT infrastructure, and a culture of innovation, all of which contribute to the rapid deployment and scaling of privacy-enhancing technologies. North America’s established position is further reinforced by ongoing collaborations between research institutions and technology vendors, ensuring continuous innovation and adaptation to evolving privacy challenges.

Asia Pacific is emerging as the fastest-growing region in the K-Anonymity Tools for Public Datasets market, projected to register a remarkable CAGR of 19.7% from 2025 to 2033. This accelerated growth is driven by exponential increases in digital data generation, rapid expansion of cloud computing, and government-led initiatives to digitize public services and healthcare records. Countries such as China, India, Japan, and South Korea are making substantial investments in AI, big data analytics, and privacy-enhancing technologies to address rising concerns over data security and individual privacy. The region’s burgeoning tech startup ecosystem, coupled with growing awareness about data anonymization among enterprises, is fostering demand for K-anonymity solutions. Additionally, the introduction of new privacy regulations and cross-border data transfer policies is prompting organizations to adopt advanced anonymization tools to ensure compliance and mitigate risks.

In emerging economies across Latin America and the Middle East & Africa, the adoption of K-anonymity tools for public datasets is gradually gaining momentum, albeit at a slower pace compared to developed regions. These markets face unique challenges, including limited awareness of privacy-enhancing technologies, budget constraints, and fragmented regulatory landscapes. Nonetheless, increasing digitalization in public administration, healthcare, and financial services is creating localized demand for privacy tools that can balance data utility with confidentiality. International collaborations, capacity-building initiatives, and donor-funded digital transformation projects are helping to bridge knowledge gaps and facilitate technology transfer. As these regions continue to modernize their data governance frameworks and enhance digital literacy, the adoption of K-anonymity solutions is expected to accelerate, unlocking new opportunities for market players.

Report Scope

Clinical Data De-Identification Pipelines Market Outlook

According to our latest research, the global clinical data de-identification pipelines market size reached USD 425.8 million in 2024. The market is experiencing robust momentum, with a recorded CAGR of 17.9% driven by the increasing adoption of advanced data privacy solutions across the healthcare sector. By 2033, the market is projected to achieve a value of USD 1,541.3 million, underscoring the escalating need for secure data handling and compliance with stringent regulatory frameworks. The primary growth factor for this sector is the rising volume of healthcare data and the critical necessity to protect patient privacy while enabling data-driven research and innovation.

The surge in healthcare digitization, coupled with the proliferation of electronic health records (EHRs), has significantly contributed to the growth of the clinical data de-identification pipelines market. Healthcare organizations are increasingly leveraging digital platforms to store, share, and analyze sensitive patient data, which in turn amplifies the risk of data breaches and unauthorized access. This scenario has heightened the demand for robust de-identification solutions, ensuring that personal health information (PHI) is rendered anonymous before being used for research, analytics, or sharing with third parties. Regulatory mandates such as HIPAA in the United States and GDPR in Europe further reinforce the need for effective data de-identification, driving both innovation and adoption in this market.

Another critical growth driver is the expanding landscape of clinical research and real-world evidence (RWE) generation. Pharmaceutical and biotechnology companies, as well as academic research institutions, rely heavily on access to vast amounts of patient data to accelerate drug development, conduct population health studies, and improve clinical outcomes. However, the sensitive nature of this data necessitates sophisticated de-identification pipelines that can efficiently strip personally identifiable information (PII) while preserving the integrity and utility of the dataset. This balance between data utility and privacy protection is fueling investments in next-generation de-identification software and services, further propelling market expansion.

The integration of artificial intelligence (AI) and machine learning (ML) technologies into de-identification pipelines is also playing a pivotal role in market growth. Advanced algorithms enable more accurate and automated identification and removal of sensitive information from unstructured clinical narratives, images, and structured datasets. This technological evolution not only enhances the scalability and reliability of de-identification processes but also addresses the growing complexity of healthcare data formats. As a result, organizations can more confidently share anonymized datasets for collaborative research, secondary analytics, and public health monitoring, all while maintaining compliance with global privacy standards.

From a regional perspective, North America continues to dominate the clinical data de-identification pipelines market, accounting for the largest share in 2024. The region’s leadership is attributed to a robust healthcare infrastructure, widespread adoption of health IT solutions, and stringent regulatory requirements surrounding data privacy. Europe follows closely, propelled by comprehensive data protection laws and strong investments in healthcare digitalization. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by burgeoning healthcare IT adoption, increasing clinical research activities, and rising awareness about patient data privacy. Latin America and the Middle East & Africa are emerging as promising markets, supported by gradual improvements in healthcare technology and regulatory frameworks.

Component Analysis

The clinical data de-identification pipelines market by component is segmented into software and services, each playing a distinct yet complementary role in the ecosystem. The software segment encompasses a wide array of solutions designed to automate the identification and removal of sensitive data from clinical records, including structured databases, unstructured clinical notes, and even medical images. These software platforms are increasingly leveraging AI and natural language processing (NLP) to enhance accuracy, adaptability, and speed, making them indispensabl

This dataset contains three healthcare datasets in Hindi and Punjabi, translated from English. The datasets cover medical diagnoses, disease names, and related healthcare information. The data has been carefully cleaned and formatted to ensure accuracy and usability for various applications, including machine learning, NLP, and healthcare analysis.

Diagnosis: Description of the medical condition or disease. Symptoms: List of symptoms associated with the diagnosis. Treatment: Common treatments or recommended procedures. Severity: Severity level of the disease (e.g., mild, moderate, severe). Risk Factors: Known risk factors associated with the condition. Language: Specifies the language of the dataset (Hindi, Punjabi, or English). The purpose of these datasets is to facilitate research and development in regional language processing, especially in the healthcare sector.

Column Descriptions: Original Data Columns: patient_id – Unique identifier for each patient. age – Age of the patient. gender – Gender of the patient (e.g., Male/Female/Other). Diagnosis – The diagnosed medical condition or disease. Remarks – Additional notes or comments from the doctor. doctor_id – Unique identifier for the doctor treating the patient. Patient History – Medical history of the patient, including previous conditions. age_group – Categorized age group (e.g., Child, Adult, Senior). gender_numeric – Numeric encoding for gender (e.g., 0 = Female, 1 = Male). symptoms – List of symptoms reported by the patient. treatment – Recommended treatment or medication. timespan – Duration of the illness or treatment period. Diagnosis Category – General category of the diagnosis (e.g., Cardiovascular, Neurological). Pseudonymized Data Columns: These columns replace personally identifiable information with anonymized versions for privacy compliance:

Pseudonymized_patient_id – An anonymized patient identifier. Pseudonymized_age – Anonymized age value. Pseudonymized_gender – Anonymized gender field. Pseudonymized_Diagnosis – Diagnosis field with anonymized identifiers. Pseudonymized_Remarks – Anonymized doctor notes. Pseudonymized_doctor_id – Anonymized doctor identifier. Pseudonymized_Patient History – Anonymized version of patient history. Pseudonymized_age_group – Anonymized version of age groups. Pseudonymized_gender_numeric – Anonymized numeric encoding of gender. Pseudonymized_symptoms – Anonymized symptom descriptions. Pseudonymized_treatment – Anonymized treatment descriptions. Pseudonymized_timespan – Anonymized illness/treatment duration. Pseudonymized_Diagnosis Category – Anonymized category of diagnosis.

This dataset contains anonymized DICOM images acquired as part of a cardiac T1 mapping study using a 5T MRI system. All personal identifiers have been removed in compliance with DICOM de-identification standards and institutional ethics approval. The dataset includes pre- and post-contrast MOLLI sequences from healthy volunteers and patients. It is made publicly available for academic and non-commercial research purposes.

BackgroundDemand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs).MethodsA publicly available dataset of magnetic resonance imaging scans for 55 HNC patients with eight segmented OARs (bilateral submandibular glands, parotid glands, level II neck lymph nodes, level III neck lymph nodes) was utilized. Eight publicly available defacing algorithms were investigated: afni_refacer, DeepDefacer, defacer, fsl_deface, mask_face, mri_deface, pydeface, and quickshear. Using a subset of scans where defacing succeeded (N=29), a 5-fold cross-validation 3D U-net based OAR auto-segmentation model was utilized to perform two main experiments: 1.) comparing original and defaced data for training when evaluated on original data; 2.) using original data for training and comparing the model evaluation on original and defaced data. Models were primarily assessed using the Dice similarity coefficient (DSC).ResultsMost defacing methods were unable to produce any usable images for evaluation, while mask_face, fsl_deface, and pydeface were unable to remove the face for 29%, 18%, and 24% of subjects, respectively. When using the original data for evaluation, the composite OAR DSC was statistically higher (p ≤ 0.05) for the model trained with the original data with a DSC of 0.760 compared to the mask_face, fsl_deface, and pydeface models with DSCs of 0.742, 0.736, and 0.449, respectively. Moreover, the model trained with original data had decreased performance (p ≤ 0.05) when evaluated on the defaced data with DSCs of 0.673, 0.693, and 0.406 for mask_face, fsl_deface, and pydeface, respectively.ConclusionDefacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods.

The Comprehensive Health Monitoring Dataset is a rich and diverse collection of health-related information designed for use in a wide range of research purposes. This dataset encompasses a variety of health indicators, providing valuable insights into the overall well-being of individuals. The dataset is meticulously curated, featuring a set of key columns that cover various aspects of health, including symptoms, vital signs, and medical parameters.

****Key Columns:****

Cough: Binary indicator (0 or 1) representing the presence or absence of cough symptoms.

Fever: Binary variable indicating the presence or absence of fever symptoms.

Difficulty Breathing: Binary measure (0 or 1) denoting the occurrence of difficulty in breathing.

Blood Pressure: Continuous numerical values representing blood pressure readings, capturing both systolic and diastolic measures.

Use Cases:

Researchers can leverage this dataset for a variety of research purposes, including but not limited to:

Epidemiological Studies: Analyzing the prevalence of common symptoms such as cough, fever, and difficulty breathing in different populations.

Disease Surveillance: Monitoring the spread of diseases by examining the dataset for patterns and trends related to specific symptoms.

Public Health Interventions: Informing public health strategies by identifying correlations between certain symptoms and health outcomes.

Ethical Considerations:

Researchers using this dataset are encouraged to adhere to ethical guidelines and privacy regulations to ensure the responsible and respectful use of health-related data. Proper anonymization and de-identification measures should be employed to protect the privacy of individuals represented in the dataset.

MultiSocial is a dataset (described in a paper) for multilingual (22 languages) machine-generated text detection benchmark in social-media domain (5 platforms). It contains 472,097 texts, of which about 58k are human-written and approximately the same amount is generated by each of 7 multilingual large language models by using 3 iterations of paraphrasing. The dataset has been anonymized to minimize amount of sensitive data by hiding email addresses, usernames, and phone numbers.

If you use this dataset in any publication, project, tool or in any other form, please, cite the paper.

Disclaimer

Due to data source (described below), the dataset may contain harmful, disinformation, or offensive content. Based on a multilingual toxicity detector, about 8% of the text samples are probably toxic (from 5% in WhatsApp to 10% in Twitter). Although we have used data sources of older date (lower probability to include machine-generated texts), the labeling (of human-written text) might not be 100% accurate. The anonymization procedure might not successfully hiden all the sensitive/personal content; thus, use the data cautiously (if feeling affected by such content, report the found issues in this regard to dpo[at]kinit.sk). The intended use if for non-commercial research purpose only.

Data Source

The human-written part consists of a pseudo-randomly selected subset of social media posts from 6 publicly available datasets:

1. Telegram data originated in Pushshift Telegram, containing 317M messages (Baumgartner et al., 2020). It contains messages from 27k+ channels. The collection started with a set of right-wing extremist and cryptocurrency channels (about 300 in total) and was expanded based on occurrence of forwarded messages from other channels. In the end, it thus contains a wide variety of topics and societal movements reflecting the data collection time.

2. Twitter data originated in CLEF2022-CheckThat! Task 1, containing 34k tweets on COVID-19 and politics (Nakov et al., 2022, combined with Sentiment140, containing 1.6M tweets on various topics (Go et al., 2009).

3. Gab data originated in the dataset containing 22M posts from Gab social network. The authors of the dataset (Zannettou et al., 2018) found out that “Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls.” They also found out that hate speech is much more prevalent there compared to Twitter, but lower than 4chan's Politically Incorrect board.

4. Discord data originated in Discord-Data, containing 51M messages. This is a long-context, anonymized, clean, multi-turn and single-turn conversational dataset based on Discord data scraped from a large variety of servers, big and small. According to the dataset authors, it contains around 0.1% of potentially toxic comments (based on the applied heuristic/classifier).

5. WhatsApp data originated in whatsapp-public-groups, containing 300k messages (Garimella & Tyson, 2018). The public dataset contains the anonymised data, collected for around 5 months from around 178 groups. Original messages were made available to us on request to dataset authors for research purposes.

From these datasets, we have pseudo-randomly sampled up to 1300 texts (up to 300 for test split and the remaining up to 1000 for train split if available) for each of the selected 22 languages (using a combination of automated approaches to detect the language) and platform. This process resulted in 61,592 human-written texts, which were further filtered out based on occurrence of some characters or their length, resulting in about 58k human-written texts.

The machine-generated part contains texts generated by 7 LLMs (Aya-101, Gemini-1.0-pro, GPT-3.5-Turbo-0125, Mistral-7B-Instruct-v0.2, opt-iml-max-30b, v5-Eagle-7B-HF, vicuna-13b). All these models were self-hosted except for GPT and Gemini, where we used the publicly available APIs. We generated the texts using 3 paraphrases of the original human-written data and then preprocessed the generated texts (filtered out cases when the generation obviously failed).

The dataset has the following fields:

• 'text' - a text sample,

• 'label' - 0 for human-written text, 1 for machine-generated text,

• 'multi_label' - a string representing a large language model that generated the text or the string "human" representing a human-written text,

• 'split' - a string identifying train or test split of the dataset for the purpose of training and evaluation respectively,

• 'language' - the ISO 639-1 language code identifying the detected language of the given text,

• 'length' - word count of the given text,

• 'source' - a string identifying the source dataset / platform of the given text,

• 'potential_noise' - 0 for text without identified noise, 1 for text with potential noise.

ToDo Statistics (under construction)