Pseudonymization is a data management and de-identification procedure by which personally identifiable information fields within a data record are replaced by one or more artificial identifiers, or pseudonyms. A single pseudonym for each replaced field or collection of replaced fields makes the data record less identifiable while remaining suitable for data analysis and data processing.
Pseudonymization (or pseudonymisation, the spelling under European guidelines) is one way to comply with the European Union's new General Data Protection Regulation (GDPR) demands for secure data storage of personal information. Pseudonymized data can be restored to its original state with the addition of information which allows individuals to be re-identified. In contrast, anonymization is intended to prevent re-identification of individuals within the dataset.
The European Data Protection Supervisor (EDPS) on 9 December 2021 highlighted pseudonymization as the top technical supplementary measure for Schrems II compliance. Less than two weeks later, the EU Commission highlighted pseudonymization as an essential element of the equivalency decision for South Korea, which is the status that was lost by the United States under the Schrems II ruling by the Court of Justice of the European Union (CJEU).
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) cloud processors or remote service providers under the "Schrems II" ruling by the CJEU. Under the GDPR and final EDPB Schrems II Guidance, the term pseudonymization requires a new protected “state” of data, producing a protected outcome that:
(1) Protects direct, indirect, and quasi-identifiers, together with characteristics and behaviors;
(2) Protects at the record and data set level versus only the field level so that the protection travels wherever the data goes, including when it is in use; and
(3) Protects against unauthorized re-identification via the Mosaic Effect by generating high entropy (uncertainty) levels by dynamically assigning different tokens at different times for various purposes.
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This advanced course will provide students with the knowledge to tackle the design of privacy-preserving ICT systems. Students will learn about existing technologies to prect privacy, and how to evalu
De-identification is the process used to prevent someone's personal identity from being revealed. For example, data produced during human subject research might be de-identified to preserve the privacy of research participants. Biological data may be de-identified in order to comply with HIPAA regulations that define and stipulate patient privacy laws. When applied to metadata or general data about identification, the process is also known as data anonymization.
Privacy (UK, US) is the ability of an individual or group to seclude themselves or information about themselves, and thereby express themselves selectively. The domain of privacy partially overlaps with security, which can include the concepts of appropriate use and protection of information. Privacy may also take the form of bodily integrity. There have been many different conceptions of privacy throughout history. Most cultures recognize the right of an individual to withhold aspects of their personal lives from public record.
Personal data, also known as personal information or personally identifiable information (PII), is any information related to an identifiable person. The abbreviation PII is widely accepted in the United States, but the phrase it abbreviates has four common variants based on personal or personally, and identifiable or identifying. Not all are equivalent, and for legal purposes the effective definitions vary depending on the jurisdiction and the purposes for which the term is being used.
Explores Privacy-Enhancing Technologies including Tor, secure computation, and homomorphic encryption.
Explores a systematic approach to privacy evaluation, emphasizing concepts like confidentiality, anonymity, and plausible deniability.
Explores the definitions, value, and challenges of privacy, including personal data and privacy properties like pseudonymity and k-anonymity.
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While public speech resources become increasingly available, there is a growing interest to preserve the privacy of the speakers, through methods that anonymize the speaker information from speech while preserving the spoken linguistic content. In this pap ...
In this thesis, we focus on the problem of achieving practical privacy guarantees in machine learning (ML), where the classic differential privacy (DP) fails to maintain a good trade-off between user privacy and data utility. Differential privacy guarantee ...