Person Re-Identification Market Outlook

According to our latest research, the global person re-identification market size in 2024 stands at approximately USD 1.47 billion, driven by the rapid adoption of advanced surveillance and analytics solutions across various sectors. The market is poised to grow at a robust CAGR of 18.2% from 2025 to 2033, reaching a forecasted value of USD 6.41 billion by the end of 2033. This impressive growth is underpinned by increasing investments in AI-driven security technologies and the rising need for efficient and accurate identification systems in public and private domains.

A primary growth factor for the person re-identification market is the escalating demand for enhanced surveillance and security systems in urban environments. With the proliferation of smart cities and the increasing complexity of urban infrastructure, governments and private organizations are investing heavily in intelligent security solutions. Person re-identification technologies enable seamless tracking and identification of individuals across multiple cameras and locations, significantly improving situational awareness and response times. This capability is especially valuable in high-traffic areas such as airports, train stations, and public events, where traditional identification methods often fall short. The integration of AI and machine learning algorithms has further amplified system accuracy, making these solutions indispensable for modern surveillance frameworks.

Another significant driver is the adoption of person re-identification technologies in the retail and transportation sectors. Retailers are leveraging these systems to analyze customer behavior, optimize store layouts, and enhance loss prevention strategies. By accurately tracking customer movement and interactions, businesses gain actionable insights that drive operational efficiency and improve customer experiences. In transportation, person re-identification is vital for managing passenger flows, ensuring safety, and streamlining access control in transit hubs. The ability to recognize individuals across different entry and exit points mitigates security risks and enhances service delivery, contributing to the sector’s growing reliance on these advanced solutions.

The evolution of deep learning and video analytics technologies has also played a pivotal role in the market's expansion. Innovations in computer vision and neural network architectures have significantly improved the accuracy and scalability of person re-identification systems. These advancements allow for real-time processing of vast amounts of video data, supporting large-scale deployments in both public and private sectors. As organizations continue to digitize their operations and embrace cloud-based solutions, the integration of person re-identification technologies with existing IT infrastructures becomes more seamless, further fueling market growth. Additionally, the decreasing cost of hardware components and the availability of scalable software platforms are making these solutions accessible to a broader range of end-users.

Regionally, Asia Pacific stands out as the fastest-growing market, propelled by extensive smart city initiatives and substantial investments in public safety infrastructure. Countries such as China, Japan, and South Korea are at the forefront of deploying advanced surveillance systems, which include person re-identification capabilities, to address urban security challenges. North America holds a significant share due to its early adoption of AI-driven security technologies and the presence of leading technology providers. Europe is also witnessing steady growth, supported by stringent regulatory frameworks and increasing adoption in transportation and government sectors. The Middle East & Africa and Latin America, while currently representing smaller shares, are expected to exhibit notable growth rates as digital transformation accelerates across these regions.

Component Analysis

The person re-identification market is segmented by component into software, hardware, and services. Software solutions currently dominate the market, accounting for the largest share due to their critical role in processing, analyzing, and managing video data for identification purposes. The rapid advancement of AI and machine learning algorithms has significantly improved the performance and reliability of re-identification software, enabling more a

Background: Scientific research in the 21st century is more data intensive and collaborative than in the past. It is important to study the data practices of researchers –data accessibility, discovery, re-use, preservation and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowing for verification of results and extending research from prior results. Methodology/Principal Findings: A total of 1329 scientists participated in this survey exploring current data sharing practices and perceptions of the barriers and enablers of data sharing. Scientists do not make their data electronically available to others for various reasons, including insufficient time and lack of funding. Most respondents are satisfied with their current processes for the initial and short-term parts of the data or research lifecycle (collecting their research data; searching for, describing or cataloging, analyzing, and short-term storage of their data) but are not satisfied with long-term data preservation. Many organizations do not provide support to their researchers for data management both in the short- and long-term. If certain conditions are met (such as formal citation and sharing reprints) respondents agree they are willing to share their data. There are also significant differences and approaches in data management practices based on primary funding agency, subject discipline, age, work focus, and world region. Conclusions/Significance: Barriers to effective data sharing and preservation are deeply rooted in the practices and culture of the research process as well as the researchers themselves. New mandates for data management plans from NSF and other federal agencies and world-wide attention to the need to share and preserve data could lead to changes. Large scale programs, such as the NSF-sponsored DataNET (including projects like DataONE) will both bring attention and resources to the issue and make it easier for scientists to apply sound data management principles.

The global Data De-identification Software market is poised for substantial growth, projected to reach approximately $5,000 million by 2025, and is anticipated to expand at a Compound Annual Growth Rate (CAGR) of around 15% through 2033. This robust expansion is primarily driven by the escalating need for data privacy and regulatory compliance across diverse industries. With the increasing volume and sensitivity of data being generated and processed, organizations are actively seeking advanced solutions to safeguard personal information while still enabling data analytics and research. Key drivers fueling this market include stringent data protection regulations such as GDPR and CCPA, a growing awareness of data privacy risks among consumers and businesses, and the increasing adoption of cloud-based solutions that offer scalability and cost-effectiveness. Furthermore, the burgeoning use of big data analytics and artificial intelligence necessitates the de-identification of data to prevent breaches and maintain ethical data handling practices. The market is characterized by a dynamic competitive landscape with a significant number of players offering a variety of solutions. The primary segmentation of the market includes cloud-based and on-premises deployment models, with cloud-based solutions gaining traction due to their flexibility and lower upfront investment. Application-wise, the software serves individuals and enterprises, with enterprises forming the dominant segment due to their extensive data management needs. Emerging trends indicate a shift towards more sophisticated de-identification techniques, including advanced anonymization and pseudonymization methods, as well as the integration of de-identification capabilities within broader data governance and security platforms. However, the market faces restraints such as the complexity of implementing de-identification techniques without compromising data utility, the high cost of advanced solutions for smaller organizations, and the potential for re-identification of anonymized data if not implemented rigorously. This comprehensive report offers an in-depth analysis of the global Data De-identification Software market, a sector projected to witness substantial growth. With an estimated market value of $2.5 billion in 2023, the market is anticipated to expand at a CAGR of 15.2%, reaching approximately $5.1 billion by 2028. This growth is driven by an escalating need for robust data privacy solutions across various industries and the increasing stringency of data protection regulations worldwide.

pone.0269097.t001 - Measuring re-identification risk using a synthetic estimator to enable data sharing

Survey researchers have long protected the privacy of respondents via de-identification (removing names and other directly identifying information) before sharing data. Although these procedures help, recent research demonstrates that they fail to protect respondents from intentional re-identification attacks, a problem that threatens to undermine vast survey enterprises in academia, government, and industry. This is especially a problem in political science because political beliefs are not merely the subject of our scholarship; they represent some of the most important information respondents want to keep private. We confirm the problem in practice by re-identifying individuals from a survey about a controversial referendum declaring life beginning at conception. We build on the concept of “differential privacy” to offer new data sharing procedures with mathematical guarantees for protecting respondent privacy and statistical validity guarantees for social scientists analyzing differentially private data. The cost of these new procedures is larger standard errors, which can be overcome with somewhat larger sample sizes.

Background: With data becoming a centerpiece of modern scientific discovery, data sharing by scientists is now a crucial element of scientific progress. This article aims to provide an in-depth examination of the practices and perceptions of data management, including data storage, data sharing, and data use and reuse by scientists around the world. Methods: The Usability and Assessment Working Group of DataONE, an NSF-funded environmental cyberinfrastructure project, distributed a survey to a multinational and multidisciplinary sample of scientific researchers in a two-waves approach in 2017-2018. We focused our analysis on examining the differences across age groups, sub-disciplines of science, and sectors of employment. Findings: Most respondents displayed what we describe as high and moderate risk data practices by storing their data on their personal computer, departmental servers or USB drives. Respondents appeared to be satisfied with short-term storage solutions; however, only half of them are satisfied with available mechanisms for storing data beyond the life of the process. Data sharing and data reuse were viewed positively: over 85% of respondents admitted they would be willing to share their data with others and said they would use data collected by others if it could be easily accessed. A vast majority of respondents felt that the lack of access to data generated by other researchers or institutions was a major impediment to progress in science at large, yet only about a half thought that it restricted their own ability to answer scientific questions. Although attitudes towards data sharing and data use and reuse are mostly positive, practice does not always support data storage, sharing, and future reuse. Assistance through data managers or data librarians, readily available data repositories for both long-term and short-term storage, and educational programs for both awareness and to help engender good data practices are clearly needed.

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

Veterinary Data De-Identification Services Market Outlook

As per our latest research, the global veterinary data de-identification services market size reached USD 428 million in 2024, and is projected to grow at a robust CAGR of 13.2% from 2025 to 2033. By the end of the forecast period, the market is expected to achieve a value of USD 1,241 million in 2033. This remarkable growth is primarily driven by increasing concerns regarding data privacy, stringent regulatory compliance requirements, and the expanding volume of veterinary health data generated through digitalization and technological advancements in animal healthcare.

The surge in demand for veterinary data de-identification services can be largely attributed to the rapid digitization of veterinary records and the proliferation of electronic health record (EHR) systems in veterinary clinics, hospitals, and research organizations. As more veterinary practices transition from paper-based to digital record-keeping, the volume of sensitive animal health information being stored and transmitted electronically has grown exponentially. This shift has heightened the risk of data breaches and unauthorized access, prompting veterinary service providers to seek robust de-identification solutions. Additionally, the integration of advanced technologies such as artificial intelligence and machine learning in veterinary diagnostics and treatment planning further amplifies the need for secure data handling and privacy protection, fueling the adoption of de-identification services.

Another key growth driver for the veterinary data de-identification services market is the evolving regulatory landscape governing animal health data. Regulatory authorities across regions have introduced or strengthened data protection guidelines to ensure the confidentiality and integrity of veterinary patient information. For instance, in the United States, the Health Insurance Portability and Accountability Act (HIPAA) and similar frameworks in Europe and Asia Pacific, such as the General Data Protection Regulation (GDPR), have set high standards for data privacy and security. Although these regulations primarily target human healthcare, their principles are increasingly being adopted in the veterinary sector, especially by research organizations and pharmaceutical companies involved in clinical trials and epidemiological studies. Compliance with these regulations necessitates the implementation of effective data de-identification strategies, which is propelling market growth.

The growing emphasis on collaborative research and data sharing within the veterinary ecosystem is also a significant factor contributing to market expansion. Veterinary clinics, diagnostic laboratories, pharmaceutical companies, and academic institutions are increasingly collaborating to advance animal health research, develop new therapeutics, and improve disease surveillance. However, data sharing initiatives require the removal of personally identifiable and sensitive information to protect animal owners' privacy and comply with ethical standards. Veterinary data de-identification services play a pivotal role in facilitating secure data exchange while minimizing the risk of re-identification. This trend is particularly pronounced in regions with strong research and development activities, such as North America and Europe, further bolstering market growth.

The role of Veterinary Healthcare in the data de-identification services market cannot be overstated. As the veterinary sector continues to evolve, the integration of comprehensive healthcare services is becoming increasingly vital. Veterinary Healthcare encompasses a wide range of services, from preventive care and diagnostics to treatment and rehabilitation, all of which generate substantial amounts of data. This data is crucial for improving animal health outcomes and advancing veterinary science. However, with the rise of digital health platforms and electronic health records, ensuring the privacy and security of this data has become a top priority. Effective de-identification strategies are essential to protect sensitive information while enabling the seamless exchange of data across the veterinary ecosystem.

From a regional perspective, North America currently dominates the veterinary data de-identification services market, accounting f

The quasi-identifiers and how they were modified to ensure a low risk of re-identification.

Anonymization Certification for Mobility Data Market Outlook

According to our latest research, the global anonymization certification for mobility data market size reached USD 1.42 billion in 2024, and is projected to grow at a robust CAGR of 18.7% from 2025 to 2033, reaching a forecasted value of USD 7.12 billion by 2033. The primary growth driver behind this expansion is the surging demand for secure, privacy-compliant data handling in the rapidly digitizing mobility sector, which encompasses public transportation, ride-sharing, autonomous vehicles, and smart city initiatives. As regulatory scrutiny intensifies worldwide, organizations are increasingly seeking reliable certification frameworks to ensure their mobility data anonymization practices meet evolving legal and ethical standards.

The growth of the anonymization certification for mobility data market is propelled by the exponential increase in mobility data generated through connected vehicles, smart transportation infrastructure, and the proliferation of mobile applications in urban environments. With the mobility ecosystem becoming more data-centric, stakeholders are under immense pressure to balance the benefits of data-driven innovation with the imperative of protecting individual privacy. This has led to a surge in demand for robust anonymization certification services that can validate the efficacy and compliance of data handling processes. Additionally, the rise of autonomous vehicles and Mobility-as-a-Service (MaaS) platforms has further intensified the need for standardized certification to mitigate privacy risks and foster public trust in emerging mobility solutions.

Another significant growth factor is the tightening regulatory landscape across major economies, particularly in regions such as Europe and North America. Legislation like the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and similar frameworks in Asia Pacific are compelling mobility data handlers to adopt stringent anonymization protocols. Certification not only serves as proof of compliance but also acts as a competitive differentiator, enabling organizations to demonstrate their commitment to privacy and data security. As a result, both private and public sector entities are increasingly investing in third-party certification to future-proof their operations against legal and reputational risks.

Technological advancements in data anonymization methods, such as differential privacy, federated learning, and advanced cryptographic techniques, are also driving market growth. These innovations are enabling more effective anonymization of complex mobility datasets, which include location, behavioral, and transaction data. The evolving threat landscape, characterized by sophisticated data re-identification attacks, is pushing organizations to seek certification from reputable bodies to validate the resilience of their anonymization strategies. Furthermore, the growing emphasis on interoperability and data sharing in smart city and logistics initiatives is making standardized certification essential for seamless collaboration among diverse stakeholders.

From a regional perspective, Europe currently leads the anonymization certification for mobility data market, driven by its rigorous regulatory environment and early adoption of smart mobility solutions. North America follows closely, fueled by the rapid expansion of ride-sharing and autonomous vehicle projects. The Asia Pacific region is emerging as a high-growth market, supported by large-scale investments in smart city infrastructure and digital transportation networks. While Latin America and the Middle East & Africa are at earlier stages of adoption, increasing urbanization and regulatory developments are expected to accelerate market growth in these regions over the forecast period.

Certification Type Analysis

The certification type segment of the anonymization certification for mobility data market is

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

This study employed an extensive literature review to identify commonalities in the data retrieval practices of users of observational data. This dataset consists of a BibTeX file with the 146 bibliographic references examined in:Gregory, K., Groth, P., Cousijn, H., Scharnhorst, A., & Wyatt, S. (2017). Searching Data: A Review of Observational Data Retrieval Practices. arxiv:1707.06937. [cs.DL]The body of literature in the dataset was retrieved using different combinations of keyword searches, primarily in the Scopus database, across all fields. Keyword searches related to information retrieval (e.g. user behavior, information seeking, information retrieval) and data practices (e.g. research practices, community practices, data sharing, data reuse) were combined with keyword searches for research data. As the terms “data” and “search” are ubiquitous in academic literature, title searches also were employed and combined with the controlled vocabulary of the database to locate relevant information. Searches in Scopus included strings such as:KEY ( user AND information ) AND TITLE-ABS-KEY ("research data" OR ( scien* W/1 data ) OR ( data W/1 ( repositor* OR archive* ) ) )TITLE ( data W/0 ( search OR retriev* OR discover* OR access* OR sharing OR reus* ) )AND ( LIMIT-TO ( EXACTKEYWORD , "Information Retrieval" ) OR LIMIT-TO ( EXACTKEYWORD , "Data Retrieval" ) OR LIMIT-TO ( EXACTKEYWORD , "Data Reuse" ) )Bibliometric techniques such as citation chaining and related records were also applied. Pertinent journals and conference proceedings not indexed within Scopus (e.g. the International Journal of Digital Curation) were searched directly using similar keywords.The approximately 400 retrieved documents were examined by close reading to identify articles referring to observational data for inclusion in the final dataset.AcknowledgementsThis work has funded by the NWO Grant 652.001.002 (programme Creative Industrie - Thematisch Onderzoek (CI-TO), Re-SEARCH: Contextual Search for Scientific Research Data)

Real-Time Data Sharing in Open Finance Market Outlook

According to our latest research, the global market size for Real-Time Data Sharing in Open Finance is valued at USD 14.7 billion in 2024, with a robust compound annual growth rate (CAGR) of 24.8% projected through the forecast period. By 2033, the market is expected to reach a valuation of USD 132.6 billion, driven by the accelerating adoption of open banking frameworks, regulatory mandates, and the increasing demand for seamless, secure, and real-time data exchange across financial ecosystems. The primary growth factor for this market is the rapid digitalization of financial services, enabling institutions to deliver personalized, efficient, and innovative solutions to consumers and businesses alike.

The evolution of open finance is fundamentally altering the financial services landscape, with real-time data sharing emerging as a pivotal enabler of innovation and efficiency. The proliferation of APIs, the standardization of data exchange protocols, and the growing consumer expectation for instant, tailored financial products are fueling the adoption of real-time data sharing solutions. Financial institutions are leveraging these capabilities to enhance customer experience, streamline operations, and introduce new revenue streams. The integration of advanced analytics and artificial intelligence with real-time data is further amplifying the value proposition, enabling hyper-personalized offerings and improved risk management. As open finance matures, the ability to share and access data in real time is becoming a core competitive differentiator, propelling market growth at an unprecedented pace.

Another significant growth driver is the regulatory impetus provided by governments and financial authorities worldwide. Initiatives such as the European Union’s PSD2, the United Kingdom’s Open Banking regulation, and similar frameworks in Asia Pacific and the Americas are mandating data portability and customer-centricity in financial services. These regulations are not only fostering a more competitive and innovative financial ecosystem but are also building consumer trust in data sharing practices. As a result, banks, fintechs, and other financial service providers are compelled to invest in robust real-time data sharing infrastructure, driving demand for software, platforms, and services that facilitate secure, compliant, and seamless data flows. The convergence of regulatory compliance and technological innovation is thus a key factor underpinning the sustained expansion of the Real-Time Data Sharing in Open Finance Market.

The market is also benefiting from the increased collaboration between traditional financial institutions and new-age fintech companies. As open finance initiatives gain traction, the ecosystem is witnessing a surge in partnerships aimed at co-creating value-added services, leveraging real-time data to deliver superior customer outcomes. This collaborative approach is accelerating the development and deployment of interoperable solutions, reducing time-to-market for innovative products, and expanding the addressable market for real-time data sharing technologies. Furthermore, the growing penetration of smartphones, internet connectivity, and digital payment channels is expanding the user base for open finance solutions, particularly in emerging markets. Collectively, these factors are shaping a dynamic and rapidly evolving market landscape, characterized by continuous innovation and fierce competition.

Regionally, North America and Europe are currently leading the adoption of real-time data sharing in open finance, driven by advanced digital infrastructure, progressive regulatory environments, and a high degree of consumer awareness. However, Asia Pacific is emerging as a high-growth region, propelled by the rapid digital transformation of financial services, supportive government initiatives, and the proliferation of fintech startups. Latin America and the Middle East & Africa are also witnessing increasing adoption, albeit at a more gradual pace, as financial inclusion initiatives and regulatory modernization gain momentum. Overall, the global market is poised for significant expansion, with regional dynamics reflecting varying levels of maturity, innovation, and regulatory readiness.

Personal Location Data Market

Data From: "There’s a Multibillion-Dollar Market for Your Phone’s Location Data"

Original source. Author: The Markup

About this dataset

There’s a multibillion-dollar market for your phone’s location data. We surveyed 100 companies to find out who they are, what they do with your data, and whether they follow best practices.

Your phone’s location is constantly being tracked and collected by hundreds of companies, many of which are unknown to you. This data is valuable—and it’s being bought and sold in a thriving industry with little regulation.

The Markup surveyed 100 companies that collect or sell location data to get a better understanding of this industry and what it means for your privacy. We asked these companies about their policies and practices around collecting, using, and selling location data. We also reviewed their public statements and website disclosures related to privacy.

What we found was an industry that lacks transparency and accountability, with few companies following best practices around protecting the privacy of their users’ data. In many cases, these companies are collecting more data than they need, retaining it for longer than necessary, sharing it with third parties without user consent, or failing to secure it properly—putting users at risk of identity theft, fraud, or other harms.

If you care about your privacy, you should know who has access to your location data—and what they’re doing with it. This dataset contains information on the 100 companies we surveyed so that you can make informed choices about which ones to trust with your personal data

How to use the dataset

This dataset contains information on companies that collect and sell location data. The data includes the company name, website, logo, narrative, company response, privacy email, privacy policy, and whether or not the company is a California-licensed data broker

Research Ideas

• To study how location data is collected and sold
• To understand the business model of location data companies
• To learn about the privacy policies of these companies

Acknowledgements

This dataset was compiled and analyzed by The Markup. The Markup is a nonprofit newsroom that investigates how powerful institutions impact our lives

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: location-data-companies.csv | Column name | Description | |:-------------------|:--------------------------------------------------------------------| | name | The name of the company. (String) | | website | The company's website. (String) | | logo | The company's logo. (String) | | narrative | A description of the company. (String) | | privacy_email | The company's privacy email address. (String) | | privacy_policy | The company's privacy policy. (String) | | CA_broker | Whether the company is a California-licensed data broker. (Boolean) |

Many initiatives encourage investigators to share their raw datasets in hopes of increasing research efficiency and quality. Despite these investments of time and money, we do not have a firm grasp of who openly shares raw research data, who doesn't, and which initiatives are correlated with high rates of data sharing. In this analysis I use bibliometric methods to identify patterns in the frequency with which investigators openly archive their raw gene expression microarray datasets after study publication. Automated methods identified 11,603 articles published between 2000 and 2009 that describe the creation of gene expression microarray data. Associated datasets in best-practice repositories were found for 25% of these articles, increasing from less than 5% in 2001 to 30%-35% in 2007-2009. Accounting for sensitivity of the automated methods, approximately 45% of recent gene expression studies made their data publicly available. First-order factor analysis on 124 diverse bibliometric attributes of the data creation articles revealed 15 factors describing authorship, funding, institution, publication, and domain environments. In multivariate regression, authors were most likely to share data if they had prior experience sharing or reusing data, if their study was published in an open access journal or a journal with a relatively strong data sharing policy, or if the study was funded by a large number of NIH grants. Authors of studies on cancer and human subjects were least likely to make their datasets available. These results suggest research data sharing levels are still low and increasing only slowly, and data is least available in areas where it could make the biggest impact. Let's learn from those with high rates of sharing to embrace the full potential of our research output.

Differential Privacy Tools Market Outlook

According to our latest research, the global differential privacy tools market size reached USD 1.42 billion in 2024, reflecting the growing prioritization of privacy-preserving data analytics across industries. The market is experiencing robust expansion, with a projected CAGR of 27.6% from 2025 to 2033. By the end of 2033, the market is anticipated to achieve a value of USD 12.83 billion, driven by the increasing need for compliance with stringent data protection regulations and the rapid adoption of advanced analytics and AI technologies. The growth of this market is underpinned by organizations’ heightened focus on balancing data utility with privacy, ensuring that sensitive information remains protected even as data-driven insights are extracted.

One of the primary growth factors for the differential privacy tools market is the escalating demand for privacy-preserving technologies in the wake of global data protection regulations such as GDPR, CCPA, and other region-specific frameworks. Enterprises are under mounting pressure to ensure that their data analytics and sharing practices do not compromise individual privacy. Differential privacy tools offer mathematically robust solutions that enable organizations to extract meaningful insights from large datasets while minimizing the risk of re-identification of personal data. This has led to accelerated adoption across sectors like healthcare, BFSI, and government, where sensitive data is routinely processed and shared. The growing frequency of data breaches and heightened consumer awareness regarding data privacy further amplify the necessity for these tools, making them a critical component of modern data management strategies.

Another significant driver is the proliferation of artificial intelligence and machine learning applications, which often require access to vast amounts of personal and sensitive data for model training and improvement. Differential privacy tools are increasingly being integrated into machine learning workflows to ensure that models can learn from data without exposing individual records. This approach not only enhances trust among stakeholders but also enables organizations to unlock the full potential of AI-driven solutions while adhering to privacy mandates. The technology’s ability to provide quantifiable privacy guarantees is particularly appealing to industries handling confidential information, such as finance and healthcare, fostering widespread adoption and investment in differential privacy solutions.

The expanding ecosystem of cloud-based data platforms and the emergence of data-sharing collaborations among enterprises and research institutions are also fueling the growth of the differential privacy tools market. As organizations look to leverage third-party analytics and cross-industry partnerships for innovation, the risk of data leakage and privacy violations increases. Differential privacy tools address these challenges by enabling secure data sharing and analysis, supporting a variety of deployment modes including both on-premises and cloud environments. The scalability and flexibility offered by modern differential privacy solutions align well with the evolving needs of digital enterprises, further accelerating market momentum and spurring continuous technological advancements.

From a regional perspective, North America currently dominates the differential privacy tools market, accounting for the largest share in 2024 due to early adoption by technology giants, robust regulatory frameworks, and significant investments in privacy-enhancing technologies. Europe follows closely, driven by stringent data protection regulations and a strong emphasis on digital sovereignty. The Asia Pacific region is witnessing the fastest growth, propelled by rapid digital transformation, rising awareness of data privacy, and increasing government initiatives to regulate data usage. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by improving digital infrastructure and growing recognition of data privacy as a strategic priority. These regional dynamics are shaping the competitive landscape and influencing the global trajectory of the differential privacy tools market.

Component Analysis

The component segment of the differential privacy tools market is bifurcated into software and services, each playing a pivotal role in the

Data Collection Methodology: USA

Data Collection Methods

The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. The National Renewable Energy Laboratory (NREL) obtains information about new stations from trade media, Clean Cities coordinators, the Submit New Station form on the Station Locator website, and through collaborating with infrastructure equipment and fuel providers, original equipment manufacturers (OEMs), and industry groups.

Users submitting updates through the "Submit New Station" or "Report a Change" forms will receive an email confirmation of their submittal. NREL will verify station details before the station is added or updated in the Station Locator. The turnaround time for updates will depend on the completeness of the information provided, as well as the responsiveness of the station or point of contact.

NREL regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies, data confirmation, and inclusion criteria may result in slight variations between NREL's database and those maintained by other organizations. NREL also collaborates with alternative fuel industry groups to identify discrepancies in data and develop data sharing processes and best practices. NREL and its data collection subcontractor are currently collaborating with natural gas, electric drive, biodiesel, ethanol, hydrogen, and propane industry groups to ensure best practices are being followed for identifying new stations and confirming station changes in the most-timely manner possible.

Station Update Schedule

Existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and providing the fuel specified. Based on an established data collection schedule, the database is updated on an ongoing basis. Stations that are no longer operational or no longer provide alternative fuel are removed from the database as they are identified.

Beginning in 2021, public, non-networked electric vehicle (EV) charging stations will be proactively verified every other year, with half of the EV charging stations verified each year. This adjustment is to accommodate the growing number of EV charging stations in the Station Locator. NREL will continue to make updates to any station record if changes are reported.

Mapping and Counting Methods

Each point on the map is counted as one station in the station count. A station appears as one point on the map, regardless of the number of fuel dispensers or electric vehicle supply equipment (EVSE) ports at that location. Station addresses are geocoded and mapped using an automatic geocoding application. The geocoding application returns the most accurate location based on the provided address. Station locations may also be provided by external sources (e.g., station operators) and/or verified in a geographic information system (GIS) tool. This information is considered highly accurate, and these coordinates override any information generated using the geocoding application.

Data Collection Methodology: Canada

Data Collection Methods

The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. National Resources Canada (NRCan) obtains information about new stations from trade media, the Submit New Station form on the Station Locator website, and through collaborating with infrastructure equipment and fuel providers, original equipment manufacturers (OEMs), and industry groups.

Users submitting updates through the "Submit New Station" or "Report a Change" forms will receive an email confirmation of their submittal. NRCan will verify station details before the station is added or updated in the Station Locator. The turnaround time for updates will depend on the completeness of the information provided, as well as the responsiveness of the station or point of contact.

NRCan regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies, data confirmation, and inclusion criteria may result in slight variations between NRCan's database and those maintained by other organizations. NRCan also collaborates with alternative fuel industry groups to identify discrepancies in data and develop data sharing processes and best practices. NRCan and its data collection subcontractor are currently collaborating with alternative fuel industry groups to ensure best practices are being followed for identifying new stations and confirming station changes in the most-timely manner possible.

Station Update Schedule

Existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and providing the fuel specified. Based on an established data c...

Consumer-Permissioned Data Sharing Market Outlook

According to our latest research, the global consumer-permissioned data sharing market size reached USD 8.2 billion in 2024 and is projected to grow at a robust CAGR of 15.4% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 28.5 billion. This rapid expansion is fueled by increasing consumer awareness regarding data privacy, regulatory pressures for transparent data usage, and the proliferation of digital platforms that require secure and consent-driven data exchange. The marketÂ’s growth trajectory is further accelerated by technological advancements and the adoption of open banking and healthcare interoperability standards, which are reshaping how organizations collect, manage, and utilize consumer data with explicit permission.

One of the primary drivers for the consumer-permissioned data sharing market is the rising demand for personalized services across various industries such as banking, healthcare, and retail. Organizations are leveraging permissioned data to tailor their offerings, enhance customer experiences, and build trust with end-users. With consumers becoming increasingly cautious about how their data is used, companies that adopt transparent and consent-based data sharing practices are witnessing higher engagement rates and improved brand loyalty. Moreover, regulatory frameworks like GDPR in Europe and CCPA in California have made it imperative for businesses to seek explicit consumer consent before accessing or sharing personal information, thereby propelling the adoption of consumer-permissioned data sharing solutions.

Technological innovation is another significant growth factor in this market. The emergence of advanced software platforms, secure APIs, and blockchain technologies has enabled more secure, efficient, and user-friendly data sharing mechanisms. These technologies not only facilitate seamless integration with existing IT infrastructures but also ensure compliance with regulatory requirements, reducing the risk of data breaches and unauthorized access. Additionally, the proliferation of fintech and healthtech startups has accelerated the adoption of consumer-permissioned data sharing solutions, as these new entrants often prioritize privacy and data security as part of their core value proposition.

The growing ecosystem of digital services is also contributing to the expansion of the consumer-permissioned data sharing market. As consumers increasingly interact with multiple digital platforms for banking, shopping, healthcare, and government services, the need for a unified and secure way to share data with explicit consent becomes paramount. This trend is further supported by the rise of open data initiatives and data portability regulations, which aim to give consumers greater control over their personal information. The integration of artificial intelligence and machine learning into data sharing platforms is enhancing the ability to manage consent, detect anomalies, and provide real-time insights, further driving market growth.

Real-Time Data Sharing in Open Finance is revolutionizing the way financial data is accessed and utilized across the industry. By enabling instantaneous data exchange between financial institutions and third-party providers, real-time data sharing enhances the ability to offer personalized financial products and services. This capability is crucial in the context of open finance, where consumers demand seamless and secure access to their financial information. As the industry continues to evolve, the integration of real-time data sharing mechanisms is expected to drive greater transparency, efficiency, and innovation, ultimately benefiting both consumers and financial service providers.

From a regional perspective, North America currently leads the global consumer-permissioned data sharing market, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of major technology providers, stringent regulatory frameworks, and high consumer awareness regarding data privacy. EuropeÂ’s market is bolstered by robust data protection laws and a mature digital infrastructure, while Asia Pacific is experiencing rapid growth due to the increasing adoption of digital banking and healthcare solutions. Emerging markets i

Origin

This poster is for SciDataCon 2023 poster exhibition

Description

Nowadays research teams everywhere face challenges in the better management of research data for cross-domain collaboration and long-term use. The research teams are often diverse in their composition as in terms of application domains, computational resources, research methods, and lab practices, just to name a few. To overcome these differences, we believe that it is essential to foster a culture of sharing experiences and ideas about research data management planning among and within the teams. By doing so, we can navigate around common barriers as well as grow data expertise together.

In this poster, we report on a joint effort between a research data repository (the depositar; https://data.depositar.io/) and a biodiversity information facility (TaiBIF; https://portal.taibif.tw/) in engaging with local research communities in fostering good data management practices. The depositar is a data repository open to researchers worldwide for the deposit, discovery, and reuse of datasets. TaiBIF (Taiwan Biodiversity Information Facility) builds essential information infrastructures and promotes the openness and integration of biodiversity data. Both teams are based in Academia Sinica, Taiwan. TaiBIF has been organizing workshops in Taiwan for the management, mobilization, application, and integration of biodiversity information. In the past years, the depositar team has been taking part in TaiBIF workshops to organize hand-on courses on writing Data Management Plans (DMPs). These workshops offer training and guidance to help researchers acquire practical skills in research data management. The course activities are designed to encourage workshop participants not only to draft DMPs but also to engage in the peer review of their draft DMPs. As a result, we empower the workshop participants to take ownership of their data management practices and contribute to the overall improvement of their data management skills.

Our templates for drafting and reviewing DMPs are derived from Science Europe's Practical Guide to the International Alignment of Research Data Management (extended edition). We have created online instructional materials where participants can simulate the process of writing DMPs based on their own research projects. Furthermore, we facilitate peer review activities in small groups by means of the DMP evaluation criteria listed in the Science Europe's guide. The entire process is conducted through open sharing, allowing participants to learn from each other and to share data management practices within their knowledge domains. Subsequently, we select outstanding DMPs from these exercises which serve as examples and discussion points for future workshops. This approach allows us to increase the availability of data management solutions that are closely aligned with specific domains. It also fosters a friendly environment that encourages researchers to organize, share, and improve upon their data management planning skills.

Reference

Science Europe. (2021). Practical Guide to the International Alignment of Research Data Management - Extended Edition. (W. W. Tu & C. H. Wang & C. J. Lee & T. R. Chuang & M. S. Ho, Trans.). https://pid.depositar.io/ark:37281/k516v4d6w

Veterinary Image De-Identification Tools Market Outlook

As per our latest research, the veterinary image de-identification tools market size globally stood at USD 245.8 million in 2024. The market is experiencing robust growth, and it is projected to reach USD 783.6 million by 2033, expanding at a remarkable CAGR of 13.5% during the forecast period from 2025 to 2033. The primary growth drivers include the rising adoption of digital imaging technologies in veterinary healthcare, stricter data privacy regulations, and the increasing demand for advanced solutions that ensure compliance and data security in veterinary practices.

The surge in veterinary digital imaging, including X-rays, CT scans, and MRIs, is fundamentally transforming animal healthcare and research. As imaging data becomes more central to diagnostics and research, the need to protect sensitive information—such as client details, animal identifiers, and proprietary research data—has grown exponentially. The implementation of data privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe and similar frameworks in North America and Asia Pacific, is compelling veterinary institutions and research bodies to adopt robust de-identification tools. These tools ensure that personal and sensitive data is removed or anonymized before images are shared for research, collaboration, or educational purposes, thereby mitigating the risk of data breaches and maintaining client trust.

Another significant growth factor for the veterinary image de-identification tools market is the increasing collaboration between veterinary hospitals, research institutes, and diagnostic centers. With a growing trend toward multi-institutional studies and the sharing of large imaging datasets for AI-based diagnostics and clinical research, maintaining data privacy and compliance is paramount. De-identification tools facilitate secure data sharing, enabling organizations to participate in global research initiatives without compromising on privacy or regulatory requirements. This is particularly important as the use of AI and machine learning in veterinary diagnostics accelerates, demanding large volumes of high-quality, de-identified imaging data for algorithm training and validation.

Furthermore, the rapid digitalization of veterinary practices, coupled with the growing awareness of cybersecurity threats, is driving the adoption of advanced de-identification solutions. Veterinary professionals are increasingly recognizing the risks associated with storing and transmitting identifiable image data, especially as telemedicine and remote consultations gain traction. The integration of de-identification tools into Picture Archiving and Communication Systems (PACS) and hospital management software is streamlining workflows, reducing manual errors, and ensuring seamless compliance with evolving regulatory standards. This trend is particularly pronounced in developed markets, where digital infrastructure and regulatory enforcement are more mature, but it is also gaining momentum in emerging economies as they modernize their veterinary healthcare systems.

From a regional perspective, North America continues to dominate the veterinary image de-identification tools market due to its advanced veterinary healthcare infrastructure, high adoption of digital imaging, and stringent data privacy laws. However, Europe is rapidly catching up, driven by strong regulatory frameworks and significant investments in veterinary research and education. The Asia Pacific region, meanwhile, is witnessing the fastest growth, propelled by increasing pet ownership, rising livestock healthcare needs, and the digital transformation of veterinary services. Latin America and the Middle East & Africa are also showing promising potential, although growth in these regions is somewhat tempered by infrastructural and regulatory challenges.

Component Analysis

The component segment of the veterinary image