k-Anonymity: a model for protecting privacy

Abstract

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, m-Argus and k-Similar provide guarantees of privacy protection.

Keywords: data anonymity, data privacy, re-identification, data fusion, privacy

Citation:
L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 557-570. Paper: 14 pages in PDF.

Related Links

• Achieving k-anonymity privacy protection using generalization and suppression

• Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Also appears as Protecting respondents identities in microdata release, IEEE Transactions on Knowledge and Data Engineering, 2001.

• Generalizing data to provide anonymity when disclosing information.

• De-identifying Facial Images (k-anonymity using microaggregation)