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.

The Data De-identification and Pseudonymization Software market is experiencing robust growth, projected to reach $1941.6 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 7.3%. This expansion is driven by increasing regulatory compliance needs (like GDPR and CCPA), heightened concerns regarding data privacy and security breaches, and the burgeoning adoption of cloud-based solutions. The market is segmented by deployment (cloud-based and on-premises) and application (large enterprises and SMEs). Cloud-based solutions are gaining significant traction due to their scalability, cost-effectiveness, and ease of implementation, while large enterprises dominate the application segment due to their greater need for robust data protection strategies and larger budgets. Key market players include established tech giants like IBM and Informatica, alongside specialized providers such as Very Good Security and Anonomatic, indicating a dynamic competitive landscape with both established and emerging players vying for market share. Geographic expansion is also a key driver, with North America currently holding a significant market share, followed by Europe and Asia Pacific. The forecast period (2025-2033) anticipates continued growth fueled by advancements in artificial intelligence and machine learning for enhanced de-identification techniques, and the increasing demand for data anonymization across various sectors like healthcare, finance, and government. The restraining factors, while present, are not expected to significantly hinder the market’s overall growth trajectory. These limitations might include the complexity of implementing robust de-identification solutions, the potential for re-identification risks despite advanced techniques, and the ongoing evolution of privacy regulations necessitating continuous adaptation of software capabilities. However, ongoing innovation and technological advancements are anticipated to mitigate these challenges. The continuous development of more sophisticated algorithms and solutions addresses re-identification vulnerabilities, while proactive industry collaboration and regulatory guidance aim to streamline implementation processes, ultimately fostering continued market expansion. The increasing adoption of data anonymization across diverse sectors, coupled with the expanding global digital landscape and related data protection needs, suggests a positive outlook for sustained market growth throughout the forecast period.

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 Data Masking Software market is experiencing robust growth, driven by increasing regulations around data privacy (like GDPR and CCPA), the expanding adoption of cloud computing, and the surging need for secure data sharing across organizations. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% during the forecast period (2025-2033). This significant growth is fueled by several key factors, including the rising demand for data anonymization and pseudonymization techniques across various sectors like banking, healthcare, and retail. Companies are increasingly investing in data masking solutions to protect sensitive customer information during testing, development, and collaboration, thus mitigating the risk of data breaches and regulatory penalties. The diverse application segments, including Banking, Financial Services, and Insurance (BFSI), Healthcare and Life Sciences, and Retail and Ecommerce, contribute significantly to market expansion. Furthermore, the shift towards cloud-based solutions offers scalability and cost-effectiveness, further accelerating market adoption. The market segmentation reveals a strong preference for cloud-based solutions, driven by their inherent flexibility and ease of deployment. Within the application segments, the BFSI sector is currently leading due to stringent regulatory compliance needs and the large volume of sensitive customer data handled. However, growth in the healthcare and life sciences sector is expected to accelerate significantly as more institutions embrace digital transformation and the handling of patient data becomes increasingly regulated. Geographic growth is robust across North America and Europe, with Asia-Pacific showing significant potential for future expansion due to growing digitalization and increasing awareness of data security issues. While the market faces certain restraints such as the complexity of implementing data masking solutions and the high initial investment costs, the long-term benefits of robust data protection and compliance outweigh these challenges, driving consistent market expansion.

The report covers Global Data Masking Tools and Technology Market and it is segmented by Type (Static, Dynamic), Deployment (Cloud, On-premise), End User Industry (BFSI, Healthcare, IT and Telecom, Retail, Government and Defense, Manufacturing, and Media and Entertainment), and by Geography. The market size and forecasts are provided in terms of value (USD million) for all the above segments.

The size and share of the market is categorized based on Application (Compliance Management, Risk Management, Identity and Access Management (IAM), Incident Response Management, Consent Management, Data Analytics) and Product (Data Discovery and Classification Tools, Consent Management Tools, Data Masking and Anonymization Tools, Data Loss Prevention (DLP) Tools, Data Governance Tools) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The global data masking tools market size was valued at USD 1231.5 million in 2025 and is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. Data masking tools are used to protect sensitive data from unauthorized access by replacing the original data with masked data that retains the same structure and statistical properties as the original data. The growing need for data privacy and compliance regulations, increasing data breaches, and rising adoption of cloud-based data storage solutions are the primary factors driving the growth of the data masking tools market. The market is segmented based on type (cloud-based and on-premises), application (large enterprises and SMEs), and region (North America, South America, Europe, Middle East & Africa, and Asia Pacific). The cloud-based segment is expected to witness significant growth during the forecast period due to its cost-effectiveness, scalability, and ease of deployment. The large enterprise segment accounted for the largest revenue share in 2025 due to the high volume of sensitive data processed by these organizations. North America is the largest regional market for data masking tools, followed by Europe. The growth in these regions is attributed to the stringent data privacy regulations and high awareness of data security risks. Asia Pacific is expected to experience rapid growth in the coming years due to the increasing adoption of data masking tools by businesses in the region.

The size and share of the market is categorized based on Type (Cloud Based, On Premises) and Application (Large Enterprises, SMEs) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The size and share of the market is categorized based on geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Global Data Masking Market size worth at USD 18.26 Billion in 2023 and projected to USD 98.91 Billion by 2032, with a CAGR of around 18.4% between 2024-2032.

The size and share of the market is categorized based on 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 geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This dataset contains anonymized survey data from a questionnaire distributed between April and June 2024. The survey aimed to uncover the range of Decision Support Tools (DSTs) utilized by water managers in drought management across various drought phases. Developed based on exploratory interviews with representatives from the national and regional water authorities in the Netherlands, the questionnaire was administered via Qualtrics, an online survey platform. It included both open-ended and closed questions, organized into three primary areas: (1) drought measures, (2) Decision Support Tools (DSTs), and (3) perspectives on model use and related uncertainties. Some questions used a Likert scale, allowing respondents to express their level of agreement from "fully disagree" (1) to "fully agree" (7). This dataset includes only the responses to questions relevant to the associated manuscript.

Global Data Masking Tool market size 2025 was XX Million. Data Masking Tool Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

This event log describes the ticketing management process of the help desk of a software company

The market size of the Data Masking Software Market is categorized based on Deployment Type (On-Premises, Cloud-Based) and Application (Compliance Management, Data Privacy Management, Test Data Management, Data Security, Analytics) and End-User Industry (BFSI, Healthcare, Retail, Telecommunications, Government) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Abstract

Since 2014, UNHCR has undertaken a comprehensive revision of the framework for monitoring UNHCR Livelihoods and Economic Inclusion programs. Since 2017, mobile data collection (survey) tools have been rolled out globally, including in Chad. The participating operations conducted a household survey to a sample of beneficiaries of each livelihoods project implemented by UNHCR and its partner. The dataset consists of baseline (331 observations) and endline data (308 observations) from the same sample beneficiaries, in order to compare before and after the project implementation and thus to measure the impact.

Geographic coverage

Amboko Amnabak Belom Djabal Doholo Dosseye Gondje Koloma Moyo

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size for this dataset is: Baseline data : 331 Endline data : 308 Total : 639

The sampling was conducted by each participating operation based on general sampling guidance provided as the following;

• At least 100 randomly selected beneficiaries for each project
• Representativeness of sub-groups (gender, camp, etc.) should be kept as much as possible
• Baseline and endline beneficiaries should be the same

Sampling deviation

Some operations may deviate from the sampling guidance due to local constraints such as logistical and security obstacles.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire used to collect the survey consists of five sections: Partner Information, General Information on Beneficiary, Access to Agricultural Production Enabled and Enhanced, Access to Self-Employment/ Business Facilitated, and Access to Wage Employment Facilitated.

Cleaning operations

The dataset presented here has undergone light checking, cleaning, harmonization of localized information, and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, and grouping values of select variables). Empty values can occur for several reasons (e.g. no occurrence of agricultural interventions among the beneficiaries will result in empty variables for the agricultural module). Local suppression did not lead to empty variables.

Response rate

Information not available

The size and share of the market is categorized based on Type (Cloud Based, On-Premises) and Application (Banking, Financial Services, Insurance, Healthcare and Life Sciences, Retail and Ecommerce, Telecommunications and IT, Government and Defense, Media and Entertainment, Manufacturing, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

BackgroundCurrent diagnostic tools are limited in their ability to diagnose cystic fibrosis liver disease (CFLD) as disease is often focal in nature. Magnetic resonance extracellular volume quantification (MRI ECV) in the liver may have diagnostic utility in CFLD as a more selective liver volume is assessed and can be performed using equipment readily available in clinical practice on a standard MRI protocol.MethodsHealthy volunteers (HV), CF participants with no liver disease (CF-noLD) and CF participants with cirrhosis (CF-C) aged 18 years and above had MRI ECV measured using a 3T Siemens scanner. An additional retrospective analysis was performed to calculate MRI ECV in individuals who had available images obtained using a 1.5T Siemens scanner from a previous study.Results16 individuals had MRI ECV measured using a 3T Siemens scanner. Mean (SD) MRI ECV was 0.316 (0.058) for HV (n  =  5), 0.297 (0.034) for CF-noLD (n  =  5) and 0.388 (0.067) for CF-C (n  = 6 ). Post-hoc analysis showed a significant difference between CF-noLD and CF-C (p  =  0.046). Of 18 individuals with available images using a 1.5T scanner, mean (SD) MRI ECV was 0.269 (0.048) in HV (n  =  8), 0.310 (0.037) in CF-noLD (n  =  8) and 0.362 (0.063) in CF-C (n  =  2).ConclusionsLiver MRI ECV quantification was feasible in adults with CF with no significant difference in results between 1.5T and 3T obtained images suggesting applicability across different types of MRI scanner. A higher MRI ECV was demonstrated in CF participants with cirrhosis suggesting potential utility as a diagnostic tool for those with advanced CFLD. Further evaluation in larger cohorts is warranted.

This file contains de-identified and anonymized healthcare facility-level raw primary data used in the analysis.