The Geospatial and Information Substitution and Anonymization Tool (GISA) incorporates techniques for obfuscating identifiable information from point data or documents, while simultaneously maintaining chosen variables to enable future use and meaningful analysis. This approach promotes collaboration and data sharing while also reducing the risk of exposure to sensitive information. GISA can be used in a number of different ways, including the anonymization of point spatial data, batch replacement/removal of user-specified terms from file names and from within file content, and aid with the selection and redaction of images and terms based on recommendations using natural language processing. Version 1 of the tool, published here, has updated functionality and enhanced capabilities to the beta version published in 2023. Please see User Documentation for further information on capabilities, as well as a guide for how to download and use the tool. If there are any feedback you would like to provide for the tool, please reach out with your feedback to edxsupport@netl.doe.gov. Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The Geospatial and Information Substitution and Anonymization Tool (GISA) was developed jointly through the U.S. DOE Office of Fossil Energy and Carbon Management’s EDX4CCS Project, in part, from the Bipartisan Infrastructure Law.

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.

The global market for data masking tools is experiencing robust growth, driven by increasing regulatory compliance needs (like GDPR and CCPA), the rising adoption of cloud computing, and the expanding volume of sensitive data requiring protection. The market, currently estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by organizations' increasing focus on data security and privacy, particularly within sectors like healthcare, finance, and government. The demand for sophisticated data masking solutions that can effectively anonymize and pseudonymize data while maintaining data utility for testing and development is a significant driver. Furthermore, the shift towards cloud-based data masking solutions, offering scalability and ease of management, is contributing to market expansion. Several key trends are shaping the market. The integration of advanced technologies such as AI and machine learning into data masking tools is enhancing their effectiveness and automating complex masking processes. The emergence of data masking solutions designed for specific data types, such as personally identifiable information (PII) and financial data, caters to niche requirements. However, challenges such as the complexity of implementing and managing data masking solutions, and concerns about the potential impact on data usability, represent restraints on market growth. The market is segmented by deployment type (cloud, on-premises), organization size (small, medium, large enterprises), and industry vertical (healthcare, finance, etc.). Key players in this space include Oracle, Delphix, BMC Software, Informatica, IBM, and several other specialized vendors offering a range of solutions to meet diverse organizational needs. The competitive landscape is dynamic, with ongoing innovation and consolidation shaping the future of the market.

The Data Masking Tools market is experiencing robust growth, driven by increasing concerns around data privacy regulations (like GDPR and CCPA) and the rising need to protect sensitive information during development, testing, and collaboration. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $8 billion by 2033. This expansion is fueled by the adoption of cloud-based solutions offering scalability and cost-effectiveness compared to on-premise deployments. Large enterprises are currently the primary adopters, but the increasing digitalization of SMEs is expected to significantly boost the market in the coming years. Key trends include the integration of AI and machine learning for more sophisticated masking techniques and the emergence of data masking solutions specifically designed for the cloud-native environment. While the market faces restraints such as the complexity of implementation and the need for skilled professionals, the overall growth trajectory remains positive, primarily driven by regulatory compliance and the need to safeguard sensitive data in an increasingly interconnected world. The competitive landscape is marked by a mix of established players like Oracle, IBM, and Informatica, and innovative startups. These companies are continuously developing advanced solutions, focusing on improving ease of use, enhancing automation, and broadening their support for various data formats and environments. Regional growth is anticipated to be strongest in North America and Europe initially, given the higher adoption rates of data privacy regulations and robust digital infrastructure. However, the Asia-Pacific region is poised for significant growth in the coming years due to rapid economic development and increasing digitalization across countries like India and China. The segment breakdown shows that cloud-based solutions dominate the market, reflecting the global shift towards cloud computing. The market is further segmented based on application in large enterprises and SMEs. Successful strategies for companies involve focusing on developing solutions compliant with evolving regulations, delivering user-friendly interfaces, and providing strong integrations with existing data management ecosystems.

Data De-Identification or Pseudonymity Software Market Outlook

As of 2023, the global Data De-Identification or Pseudonymity Software market is valued at approximately USD 1.5 billion and is projected to grow at a robust CAGR of 18% from 2024 to 2032, driven by increasing data privacy concerns and stringent regulatory requirements.

The growth of the Data De-Identification or Pseudonymity Software market is primarily fueled by the exponential increase in data generation across industries. With the advent of IoT, AI, and digital transformation strategies, the volume of data generated has seen an unprecedented spike. Organizations are now more aware of the need to protect sensitive information to comply with global data privacy regulations such as GDPR in Europe and CCPA in California. The need to ensure that personal data is anonymized or de-identified before analysis or sharing has escalated, pushing the demand for these software solutions.

Another significant growth factor is the rising number of cyber-attacks and data breaches. As data becomes more valuable, it also becomes a prime target for cybercriminals. In response, companies are investing heavily in data privacy and security measures, including de-identification and pseudonymity solutions, to mitigate risks associated with data breaches. This trend is more prevalent in sectors dealing with highly sensitive information like healthcare, finance, and government. Ensuring that data remains secure and private while being useful for analytics is a key driver for the adoption of these technologies.

Moreover, the evolution of Big Data analytics and cloud computing is also spurring growth in this market. As organizations move their operations to the cloud and leverage big data for decision-making, the importance of maintaining data privacy while utilizing large datasets for analytics cannot be overstated. Cloud-based de-identification solutions offer scalability, flexibility, and cost-effectiveness, making them increasingly popular among enterprises of all sizes. This shift towards cloud deployments is expected to further boost market growth.

Regionally, North America holds the largest market share due to its advanced technological infrastructure and stringent data protection laws. The presence of major technology companies and a high rate of adoption of advanced solutions in the U.S. and Canada contribute significantly to regional market growth. Europe follows closely, driven by rigorous GDPR compliance requirements. The Asia Pacific region is anticipated to witness the fastest growth, attributed to the increasing digitization and growing awareness about data privacy in countries like India and China.

As organizations increasingly seek to protect their sensitive data, the concept of Data Protection on Demand is gaining traction. This model allows businesses to access data protection services as and when needed, providing flexibility and scalability. By leveraging cloud-based platforms, companies can implement robust data protection measures without the need for significant upfront investments in infrastructure. This approach not only ensures compliance with data privacy regulations but also offers a cost-effective solution for managing data security. As the demand for on-demand services continues to rise, Data Protection on Demand is poised to become a critical component of data management strategies across various industries.

Component Analysis

The Data De-Identification or Pseudonymity Software market by component is segmented into software and services. The software segment dominates the market, driven by the increasing need for automated solutions that ensure data privacy. These software solutions come with a variety of tools and features designed to anonymize or pseudonymize data efficiently, making them essential for organizations managing large volumes of sensitive information. The software market is expanding rapidly, with new innovations and improvements constantly being introduced to enhance functionality and user experience.

The services segment, though smaller compared to software, plays a crucial role in the market. Services include consulting, implementation, and maintenance, which are essential for the successful deployment and operation of de-identification software. These services help organizations tailor the software to their specific needs, ensuring compliance with regional and industry-specific data protection regulations.

Data De-identification and Pseudonymity Software Market Outlook

The global data de-identification and pseudonymity software market is projected to grow significantly, reaching approximately USD 4.2 billion by 2032, driven primarily by increasing data privacy concerns and stringent regulatory requirements worldwide.

The primary growth factor in the data de-identification and pseudonymity software market is the surge in data breaches and cyber-attacks. With the exponential increase in data generation, organizations are more vulnerable to data breaches and unauthorized access. These security concerns have prompted businesses and governments to invest heavily in robust data protection solutions. Data de-identification and pseudonymity software provide a secure way to anonymize sensitive information, making it less susceptible to malicious activities. As data protection laws become more rigorous, the demand for such technologies will continue to rise, further propelling market growth.

Another significant factor contributing to market growth is the growing awareness and emphasis on data privacy among consumers. In recent years, consumers have become increasingly aware of how their data is being used and the potential risks associated with data misuse. This heightened awareness has put pressure on organizations to adopt comprehensive data protection measures. Data de-identification and pseudonymity software offer a means to protect personal information while still allowing organizations to utilize data for analytics and decision-making. This dual benefit is a key driver for the adoption of these technologies across various sectors.

Moreover, regulatory compliance is a crucial driver for the market. Regulations such as the General Data Protection Regulation (GDPR) in Europe, the Health Insurance Portability and Accountability Act (HIPAA) in the United States, and various other data protection laws worldwide mandate stringent measures for data protection. Non-compliance can result in hefty fines and legal repercussions. Therefore, organizations are increasingly adopting data de-identification and pseudonymity software to ensure compliance with these regulations. The need for regulatory compliance is expected to sustain market growth in the foreseeable future.

Regionally, North America currently dominates the global data de-identification and pseudonymity software market, accounting for the largest market share. This is attributed to the presence of major technology players, stringent data protection regulations, and high adoption rates of advanced technologies in the region. Europe follows closely, with significant market contributions from countries such as Germany, France, and the UK, driven by robust regulatory frameworks like GDPR. The Asia Pacific region is also expected to witness substantial growth, fueled by rapid digitalization, increasing cybersecurity threats, and growing awareness about data privacy in countries like China, India, and Japan.

Data Masking Tools play a pivotal role in enhancing the security framework of organizations by providing an additional layer of protection for sensitive information. These tools are designed to obscure specific data within a dataset, ensuring that unauthorized users cannot access or decipher the original information. As businesses increasingly rely on data-driven insights, the need for robust data masking solutions becomes more critical. By employing data masking tools, organizations can safely share data across departments or with third-party vendors without compromising privacy. This capability is especially beneficial in industries such as healthcare and finance, where data privacy is paramount. The integration of data masking tools with existing data protection strategies can significantly reduce the risk of data breaches and ensure compliance with regulatory standards.

Component Analysis

The data de-identification and pseudonymity software market can be segmented by component into software and services. The software segment is anticipated to hold the lion's share due to the increasing adoption of data protection solutions across various industries. Software solutions provide automated tools for anonymizing and pseudonymizing data, ensuring compliance with regulatory standards. These solutions are essential for organizations aiming to mitigate the risks associated with data breaches and unauthorized access. As cyber threats continue to evolve, the demand for advanced software solutions is exp

The size and share of this market is categorized based on Compliance Management (Regulatory Compliance, Policy Management, Audit Management, Risk Management, Data Breach Management) and Data Governance (Data Classification, Data Quality Management, Metadata Management, Data Stewardship, Data Lifecycle Management) and Privacy Management (Consent Management, Incident Response, Privacy Impact Assessments, Third-Party Risk Management, User Rights Management) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

The market for SAP Selective Test Data Management Tools is experiencing robust growth, driven by increasing regulatory compliance needs, the expanding adoption of agile and DevOps methodologies, and the rising demand for faster and more efficient software testing processes. The market size in 2025 is estimated at $1.5 billion, projecting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by the increasing complexity of SAP systems and the associated challenges in managing test data effectively. Large enterprises are the primary adopters of these tools, representing a significant portion of the market share, followed by medium-sized and small enterprises. The cloud-based deployment model is gaining traction due to its scalability, cost-effectiveness, and ease of access, surpassing on-premises solutions in growth rate. Key players like SAP, Informatica, and Qlik are actively shaping the market through continuous product innovation and strategic partnerships. However, challenges remain, including the high initial investment costs associated with implementing these tools, the need for specialized expertise, and data security concerns. The geographic distribution reveals North America as a dominant region, followed by Europe and Asia Pacific. Growth in the Asia Pacific region is anticipated to be particularly strong, driven by increasing digitalization and the expanding adoption of SAP solutions across various industries. The competitive landscape is marked by both established vendors and emerging players, leading to increased innovation and a wider array of solutions to meet diverse customer needs. The market is expected to continue its trajectory of growth, driven by factors such as the increasing adoption of cloud-based solutions, the growing demand for data masking and anonymization techniques, and the rising emphasis on test data quality and compliance. Companies are actively seeking solutions that streamline their testing processes, reduce costs, and minimize risks associated with inadequate test data management.

The Test Data Generation Tools market is experiencing robust growth, driven by the increasing demand for high-quality software and the rising adoption of agile and DevOps methodologies. The market's expansion is fueled by several factors, including the need for realistic and representative test data to ensure thorough software testing, the growing complexity of applications, and the increasing pressure to accelerate software delivery cycles. The market is segmented by type (Random, Pathwise, Goal, Intelligent) and application (Large Enterprises, SMEs), each demonstrating unique growth trajectories. Intelligent test data generation, offering advanced capabilities like data masking and synthetic data creation, is gaining significant traction, while large enterprises are leading the adoption due to their higher testing volumes and budgets. Geographically, North America and Europe currently hold the largest market shares, but the Asia-Pacific region is expected to witness significant growth due to rapid digitalization and increasing software development activities. Competitive intensity is high, with a mix of established players like IBM and Informatica and emerging innovative companies continuously introducing advanced features and functionalities. The market's growth is, however, constrained by challenges such as the complexity of implementing and managing test data generation tools and the need for specialized expertise. Overall, the market is projected to maintain a healthy growth rate throughout the forecast period (2025-2033), driven by continuous technological advancements and evolving software testing requirements. While the precise CAGR isn't provided, assuming a conservative yet realistic CAGR of 15% based on industry trends and the factors mentioned above, the market is poised for significant expansion. This growth will be fueled by the increasing adoption of cloud-based solutions, improved data masking techniques for enhanced security and privacy, and the rise of AI-powered test data generation tools that automatically create comprehensive and realistic datasets. The competitive landscape will continue to evolve, with mergers and acquisitions likely shaping the market structure. Furthermore, the focus on data privacy regulations will influence the development and adoption of advanced data anonymization and synthetic data generation techniques. The market will see further segmentation as specialized tools catering to specific industry needs (e.g., financial services, healthcare) emerge. The long-term outlook for the Test Data Generation Tools market remains positive, driven by the relentless demand for higher software quality and faster development cycles.

The market for SAP Selective Test Data Management Tools is experiencing robust growth, driven by the increasing need for efficient and secure testing environments within organizations leveraging SAP systems. The rising complexity of SAP landscapes, coupled with stringent regulatory compliance requirements, necessitates specialized tools to manage and anonymize sensitive data used in testing. This ensures compliance with data privacy regulations like GDPR and CCPA while maintaining data integrity for accurate test results. The market is segmented by deployment (cloud, on-premise), organization size (small, medium, large), and industry vertical (BFSI, retail, healthcare, etc.). While precise figures are unavailable, considering a typical CAGR of 15% in similar enterprise software segments and a current market size estimated within the $500-$700 million range in 2025, we can project substantial growth over the forecast period (2025-2033). Key players like IntelliCorp, SAP, Qlik, Informatica, and others are actively competing, fostering innovation and driving market expansion. The adoption of Agile and DevOps methodologies further fuels demand for streamlined test data management processes, contributing significantly to market growth.
The competitive landscape is characterized by a mix of established players and emerging niche providers. Larger vendors often integrate their solutions within broader enterprise software portfolios, while smaller players specialize in specific functionalities or cater to particular industry needs. Challenges include the integration complexities associated with legacy SAP systems and the need for continuous upskilling to handle evolving technological advancements. Despite these hurdles, the long-term outlook for the SAP Selective Test Data Management Tools market remains positive, fueled by increasing digital transformation initiatives, growing data volumes, and the critical need for robust testing environments to ensure the reliable performance of SAP systems. The market will likely see further consolidation as larger players acquire smaller, specialized vendors to broaden their product offerings.

This FAIRsharing record describes: Integrating Data for Analysis, Anonymization and SHaring (iDASH) is one of the National Centers for Biomedical Computing (NCBC) under the NIH Roadmap for Bioinformatics and Computational Biology. Founded in 2010, the iDASH center is hosted on the campus of the University of California, San Diego and addresses fundamental challenges to research progress and enables global collaborations anywhere and anytime. Driving biological projects motivate, inform, and support tool development in iDASH. iDASH collaborates with other NCBCs and disseminates tools via annual workshops, presentations at major conferences, and scientific publications. iDASH offers a secure cyberinfrastructure and tools to support a privacy-preserving data repository and open source software. iDASH also is active in research and training in its mission area.

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.

La taille et la part de marché sont classées selon Type (Data encryption tools, Data masking solutions, Privacy compliance software, Anonymization tools, Data protection platforms) and Application (Data protection, Compliance management, Risk mitigation, Privacy management, Regulatory adherence) and régions géographiques (Amérique du Nord, Europe, Asie-Pacifique, Amérique du Sud, Moyen-Orient et Afrique).

Размер и доля сегментированы по Type (Data encryption tools, Data masking solutions, Privacy compliance software, Anonymization tools, Data protection platforms) and Application (Data protection, Compliance management, Risk mitigation, Privacy management, Regulatory adherence) and регионам (Северная Америка, Европа, Азиатско-Тихоокеанский регион, Южная Америка, Ближний Восток и Африка)

Explore the growth potential of Market Research Intellect's IoT Data Governance Market Report, valued at USD 2.5 billion in 2024, with a forecasted market size of USD 8.5 billion by 2033, growing at a CAGR of 15.5% from 2026 to 2033.

この市場の規模とシェアは、次の基準で分類されます： Type (Data encryption tools, Data masking solutions, Privacy compliance software, Anonymization tools, Data protection platforms) and Application (Data protection, Compliance management, Risk mitigation, Privacy management, Regulatory adherence) and 地域別（北米、欧州、アジア太平洋、南米、中東およびアフリカ）