k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
Open accessAbstract
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.
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Two approaches to ensure privacy in the context of LA appear in the literature: privacy-preserving data publishing and privacy-preserving data mining (differential privacy).
“Two approaches to ensure privacy in the context of LA appear in the literature.”
From Federated Learning for Data Analytics in Education (2023) · cites this paper as [4]
Both privacy and security are popular topics of research and several approaches have been proposed including anonymity frameworks, differential privacy, and blockchain-based solutions.
“Both privacy and security are popular topics of research and several approaches have been proposed including anonymity frameworks [203] to more recently proposed differential privacy [204], and blockchain-based solutions [205].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [203]
Key approaches seek to protect specific individuals and groups from being identifiable, i.e., minimize disclosure risk.
“Key approaches (e.g.[194,205,276]) seek to protect speciic individuals and groups from being identiiable, i.e., minimize disclosure risk.”
From Fairness in machine learning: A survey (2024) · cites this paper as [276]
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“K-anonymity, introduced by Sweeney (2002), requires that each record be indistinguishable from at least k − 1 other records with respect to quasi-identifier attributes.”
- Authors
- SWEENEY, L.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- SWEENEY, LATANYA
- ORCID
- SWEENEY, LATANYA
- Venue
- International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
- Publisher
- World Scientific Pub Co Pte Ltd
- Published
- 2002-10
- Volume
- 10
- Issue
- 05
- Pages
- 557-570
- ISSN
- 0218-4885 (ISSN-L)
- 1793-6411
- OpenAlex
- W2159024459
- Cited by
- 8,571
- References
- 17
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- FWCI
- 28.26
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
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- cc-by
- Locations
- International Journal of Uncertainty Fuzziness and Knowledge-Based Systems (publishedVersion)
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- Figshare (submittedVersion, cc-by)
- KiltHub Repository
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Keywords
- k-anonymity, privacy preservation, re-identification attacks, data anonymization, privacy-preserving data sharing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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