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The importance of GDPR-compliant pseudonymization increased dramatically in June 2021 when the European Data Protection Board (EDPB) and the European Commission highlighted GDPR-compliant Pseudonymisation as the state-of-the-art technical supplementary measure for the ongoing lawful use of EU personal data when using third country (i.e., non-EU ...
According to the GDPR, pseudonymisation is a required process for stored data that transforms personal data in such a way that the resulting data cannot be attributed to a specific data subject without the use of additional information (as an alternative to the other option of complete data anonymisation). [30]
Reference is made to codes of conduct as a tool to set out possible anonymisation mechanisms as well as retention in a form in which identification of the data subject is “no longer possible”. [5] There are five types of data anonymization operations: generalization, suppression, anatomization, permutation, and perturbation. [6]
A pseudonym (/ ˈ sj uː d ə n ɪ m /; from Ancient Greek ψευδώνυμος (pseudṓnumos) 'lit. falsely named') or alias (/ ˈ eɪ l i. ə s /) is a fictitious name that a person assumes for a particular purpose, which differs from their original or true meaning ().
When surveys are conducted, such as a census, they collect information about a specific group of people.To encourage participation and to protect the privacy of survey respondents, the researchers attempt to design the survey in a way that when people participate in a survey, it will not be possible to match any participant's individual response(s) with any data published.
Once an individual's privacy has been breached as a result of re-identification, future breaches become much easier: once a link is made between one piece of data and a person's real identity, any association between the data and an anonymous identity breaks the anonymity of the person.
To use k-anonymity to process a dataset so that it can be released with privacy protection, a data scientist must first examine the dataset and decide whether each attribute (column) is an identifier (identifying), a non-identifier (not-identifying), or a quasi-identifier (somewhat identifying).
Confidential computing is a security and privacy-enhancing computational technique focused on protecting data in use.Confidential computing can be used in conjunction with storage and network encryption, which protect data at rest and data in transit respectively.