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Top 15 Data De-Identification Tools SaaS Companies in May 2026

As of May 2026, there are 15 SaaS companies in Data De-Identification Tools. They have combined revenues of $147.3M and employ 1.1K people. They have raised $107.1M and serve - customers combined.

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.

Companies
15
Revenue
$147.3M
Funding
$107.1M
Employees
1.1K

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Top Data De-Identification Tools Companies

Showing 10 of 3 companies ranked by annual revenue.

1
Duality Technologies

Hoboken, New Jersey, United States

Duality's breakthrough innovative technologies eliminate the conflict between data protection and business growth and innovation. The Duality Data Analytics and AI platform is built upon advanced encryption methods, hardware technologies, and machine learning techniques that protect sensitive data while in use. Duality is the only multi-PET platform with the ability to combine various technologies to meet the unique needs of sensitive data operations. These guardrails streamline and enhance the data operations critical for data-driven insights and innovations by eliminating bulky, expensive, and limiting processes like data anonymization and tokenization. Traditional data protection methods prevent organizations from truly adopting and leveraging advanced models to their benefit, resulting in restrictive policies like "no sensitive data can be used for model training." With Duality, organizations can confidently customize 3rd party models on their own data without fear of data leaks. Model providers can scale model customization knowing that their proprietary model is never exposed to the customer, preventing competitive intelligence leaks. Financial institutions can turn their manual KYC requests into self-service operations, greatly enhancing the speed and success of these expensive requirements. The benefits of data protection guardrails span from efficiency gains, to unlocking previously inaccessible data, to slashing costs on high-security infrastructure.

Revenue
$5.6M
Customers
-
Year founded
2016
Funding
-
Team size
51
Growth
-
2
Datahash

Dubai, Dubai, United Arab Emirates

Datahash is a bootstrapped, profitable SaaS company headquartered in Dubai, specializing in privacy-safe first-party data infrastructure.

Revenue
$5.5M
Customers
-
Year founded
2019
Funding
-
Team size
50
Growth
-
3
Oblivious

Dublin, Ireland

Oblivious builds tools to allow you to use your sensitive data with internal and external partners while keeping it private and secure

Revenue
$5.2M
Customers
-
Year founded
2020
Funding
$1.1M
Team size
32
Growth
48.98%

Inclusion Criteria

- 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

Data De-Identification Tools SaaS Companies | GetLatka