Stamford, Connecticut, United States
Unleash the Power of Secure Data We safeguard privacy, ensure data is protected everywhere, and fuel innovation with secure AI.
- Revenue
- $50.7M
- Year founded
- 1996
- Funding
- $6M
- Team size
- 378
As of September 2026, Latka tracks 3 data De-Identification tools companies with $10M–$100M in annual revenue. They have combined revenues of $111.2M and employ 720 people. They have raised $86M.
Every company below sells data De-Identification tools to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.
Data De-Identification Tools are specialized software solutions designed to remove or obscure personally identifiable information (PII) from datasets, enabling organizations to protect individual privacy while making data available for analysis and research. These tools are essential in various sectors, including healthcare, finance, and education, where sensitive information needs to be handled in compliance with privacy regulations such as HIPAA and GDPR. The primary use cases for Data De-Identification Tools include preparing data for research, analytics, and machine learning while ensuring that the data cannot be traced back to any individual. Typical features of these tools include data masking, tokenization, pseudonymization, and the application of data minimization principles. Common buyer personas include data privacy officers, compliance managers, and IT security professionals who are responsible for safeguarding sensitive data assets within their organizations. Organizations that utilize these tools benefit from improved data security, enhanced compliance with legal requirements, and the ability to share data in an anonymized form that still retains its utility for analysis and decision-making. The adoption of Data De-Identification Tools is becoming increasingly critical as data breaches and privacy concerns continue to rise in today’s digital landscape.
Sorting: Highest -> Lowest
Showing 3 of 3 companies ranked by annual revenue.
Stamford, Connecticut, United States
Unleash the Power of Secure Data We safeguard privacy, ensure data is protected everywhere, and fuel innovation with secure AI.
San Francisco, California, United States
Very Good Security is a fintech & data security enablement platform helping to maximize the value of data without compliance & breach risk.
Tampa, Florida, United States
Developer of enterprise data management software intended to help businesses reduce their sensitive data footprint. The company's software focuses on discovering, classifying, monitoring and protecting personal information, medical records, credit card numbers, and intellectual property stored across the enterprise, within the e-mail, and in the cloud, and specializes in the high-precision search and automated classification of unstructured data, enabling clients in the healthcare, public sector, retail, education, financial services, energy, industrial, and entertainment markets to proactively minimize the risks, costs and reputational damage of successful cyber attacks
Latka tracks 3 data De-Identification tools companies with $10M–$100M in annual revenue. Together they generate $111.2M in annual revenue and employ 720 people.
Protegrity Usa, Inc. is the largest, with $50.7M in annual revenue, founded in 1996.
The average data De-Identification tools company in this list makes $37.1M a year, across 3 companies with reported revenue.
Ranked by annual revenue, the leaders are Protegrity Usa, Inc., Very Good Security and Spirion.
The 3 data De-Identification tools companies with $10M–$100M in annual revenue tracked here have raised $86M in disclosed funding between them.
- Must remove or obscure identifiable information to protect individual privacy - Should comply with industry regulations (e.g., HIPAA, GDPR) governing data privacy - Must provide options for data masking, tokenization, or pseudonymization techniques - Should enable organizations to analyze and utilize data without revealing personal identifiers - Not just focused on data storage; must also ensure compliance throughout data processing workflows - Should support multiple data formats and sources to serve diverse organizational needs - Designed for use by data privacy officers and compliance managers in their efforts to safeguard sensitive information
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