De-Identification of Personal Information
By Simson L. Garfinkel. 2015
165 citations recorded by the source corpus.
De-identification removes identifying information from a dataset so that individual data cannot be linked with specific individuals. De-identification can reduce the privacy risk associated with collecting, processing, archiving, distributing or publishing information. De-identification thus attempts to balance the contradictory goals of using and sharing personal information while protecting privacy. Several U.S laws, regulations and policies specify that data should be deidentified prior to sharing. In recent years researchers have shown that some de-identified data can sometimes be re-identified. Many different kinds of information can be de-identified, including structured information, free format text, multimedia, and medical imagery. This document summarizes roughly two decades of de-identification research, discusses current practices, and presents opportunities for future research.
Subfields: Information Systems, Privacy & Security
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Citations in the corpus
- Trustworthy Artificial Intelligence: A Review (2022, methodology): “One way to ensure data privacy is through de-identification techniques that remove personal identifiers and their associations from the data [74].”
- A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism (2020, background): “• Anonymization and de-identification of • How can we make AI systems secure? personal data (Garfinkel, 2015; Khalil & • How can AI systems be robust against Ebner, 2016) exploitation, threats of cyberattacks, and • Human-in-the-loop or human-on-the-loop privacy violation?”