// THREAT DETECTION AND DATA PRIVACY TERM
Anonymization
Anonymization is the process of removing or modifying personally identifiable information from data so that the individual cannot be directly or indirectly identified, making the data safe for sharing or analysis without privacy risks.
TECHNICAL DEFINITION
Anonymization is a data privacy technique involving the irreversible transformation of personally identifiable information (PII) within a dataset to prevent re-identification of data subjects, thereby reducing privacy risks and enabling compliance with regulations such as GDPR and HIPAA for data sharing, analytics, and research.
BACKGROUND
Computer security is a subdiscipline within the field of information security. It focuses on protecting computer software, systems, and networks from threats that can lead to unauthorized information disclosure, theft, or damage to hardware, software, or data, as well as to the disruption or misdirection of the services they provide.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- De-identification
- Data masking
- Data scrambling
- Data obfuscation
- Pseudonymization (often considered a step towards)
- Privacy-enhancing technologies (PETs)
USAGE NOTE
Anonymization is crucial for compliance with data protection regulations and for safely sharing sensitive data for research or analytics without compromising individual privacy.
DEVELOPERS
Organizations developing technology related to Anonymization.
Privitar specializes in data privacy and anonymization solutions, enabling organizations to use sensitive data safely for analytics, research, and machine learning while complying with privacy regulations. Their platform applies advanced anonymization techniques such as k-anonymity and differential privacy.
Duality Technologies offers secure data collaboration platforms utilizing homomorphic encryption and secure multi-party computation (SMPC) to enable analysis on sensitive data without decrypting or exposing the underlying information, effectively providing a strong form of data anonymization during computation.
Inpher develops privacy-preserving machine learning and analytics platforms using secure multi-party computation (SMPC) and homomorphic encryption. Their technology allows multiple parties to compute on their combined data without revealing individual data points, offering advanced anonymization capabilities.
Sarus Technologies provides a platform for privacy-preserving AI, focusing on synthetic data generation and anonymization techniques. Their solutions enable data scientists to work with realistic, yet fully anonymized, data for model training and development.
TripleBlind offers a privacy-enhancing technology platform that allows organizations to collaborate on sensitive data without decryption. Using advanced cryptographic techniques like homomorphic encryption and secure multi-party computation, it ensures data remains anonymized and protected throughout the analysis process.
IBM develops a range of cybersecurity and data privacy solutions, including technologies for data anonymization and de-identification. Their offerings often integrate techniques like generalization, suppression, and data masking to protect sensitive information in various applications.
Microsoft Research actively develops and publishes cutting-edge research in privacy-enhancing technologies, including significant contributions to differential privacy, secure multi-party computation, and other anonymization methods that influence product development across Microsoft's ecosystem.
NIST, a U.S. government agency, develops standards, guidelines, and frameworks for cybersecurity and privacy, including comprehensive work on data de-identification and anonymization techniques to help organizations protect sensitive data and comply with regulations.