Data anonymization is a type of information sanitization whose intent is privacy protection. It is the process of either encrypting or removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous.
Data anonymization has been defined as a "process by which personal data is irreversibly altered in such a way that a data subject can no longer be identified directly or indirectly, either by the data controller alone or in collaboration with any other party."  Data anonymization enables the transfer of information across a boundary, such as between two departments within an agency or between two agencies, while reducing the risk of unintended disclosure, and in certain environments in a manner that enables evaluation and analytics post-anonymization.
In the context of medical data, anonymized data refers to data from which the patient cannot be identified by the recipient of the information. The name, address, and full post code must be removed, together with any other information which, in conjunction with other data held by or disclosed to the recipient, could identify the patient..
De-anonymization is the reverse process in which anonymous data is cross-referenced with other data sources to re-identify the anonymous data source. Generalization and perturbation are the two popular anonymization approaches for relational data.. The process of obscuring data with the ability to re-identify it later is also called pseudonymization and is one way companies can store data in a way that is HIPAA compliant.
Data anonymization tools
- CA Data Manager, CA Technologies
- Data Base Protector, Protegrity
- Dynamic Data Masking, Informatica
- IBM Security Guardium, IBM
- IMASK, Mentis
- IRI Field Shield, IRI
- Micro Focus Data Express™, Micro Focus
- Oracle Advanced Security, Oracle
- Privacy Analytics Eclipse, Privacy Analytics
- Privitar Publisher , Privitar Ltd
- Thales eSecurity, Thales
- Soflab GALL, Soflab Technology
- Differential privacy
- Fillet (redaction)
- Masking and unmasking by intelligence agencies
- Statistical disclosure control
- Data science under GDPR with pseudonymization in the data pipeline Published by Dativa, 17 April, 2018
- ISO 25237:2017 Health informatics -- Pseudonymization. ISO. 2017. p. 7.
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- Raghunathan, Balaji (June 2013). The Complete Book of Data Anonymization: From Planning to Implementation. CRC Press. ISBN 9781482218565.
- Khaled El Emam, Luk Arbuckle (August 2014). Anonymizing Health Data: Case Studies and Methods to Get You Started. O'Reilly Media. ISBN 978-1-4493-6307-9.
- Rolf H. Weber, Ulrike I. Heinrich (2012). Anonymization: SpringerBriefs in Cybersecurity. Springer. ISBN 9781447140665.
- Aris Gkoulalas-Divanis, Grigorios Loukides (2012). Anonymization of Electronic Medical Records to Support Clinical Analysis (SpringerBriefs in Electrical and Computer Engineering). Springer. ISBN 9781461456674.
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