2002/10/01 by Latanya Sweeney, LATANYA SWEENEY · 13 citations
Computer Science · Decision Sciences · Social Sciences · #Anonymity #Computer science #Computer security #Data Protection Act 1998 #Data Quality and Management #Identification (biology) #Information privacy #Internet privacy #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #Private information retrieval #Set (abstract data type) #Software deployment #k-anonymity
paper · doi:10.1142/s0218488502001648
crossref issued 2002/10/01 · crossref published 2002/10/01 · crossref published-print 2002/10/01 · openalex publication_date 2002/10/01 · crossref created 2002/11/18 · crossref published-online 2012/05/02 · crossref deposited 2019/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06 · crossref indexed 2026/08/06
Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k-anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, μ-Argus and k-Similar provide guarantees of privacy protection.