k-Anonymity¶
SWEENEY, L. (2002). k-Anonymity: A Model for Protecting Privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557-570.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Identifiability
- The same dual structure governs cryptographic indistinguishability (plaintexts kept in one large class given ciphertext) and k-anonymity (each record made indistinguishable among at least \(k\)).
This sourceDefines k-anonymity: each record made indistinguishable among at least k — a minimum-size equivalence-class privacy guarantee.
Supported in partVerified against the work's full text
Sweeney defines k-anonymity as each record being indistinguishable from at least k-1 others, but says nothing about cryptographic indistinguishability or a shared dual structure.
“the information for each person contained in the release ca nnot be distinguished from at least k-1 individuals whose information also appears in the release”
- The same dual structure governs cryptographic indistinguishability (plaintexts kept in one large class given ciphertext) and k-anonymity (each record made indistinguishable among at least \(k\)).
- Vouched Anonymity
- Protection is the size of the surviving set, which makes concealment a quantity rather than a state, and is why deleting a name changes nothing once the intersection is a singleton.
This sourceShows that combinations of a few quasi-identifying attributes often single out individuals, so data stripped of explicit identifiers can still be re-identified by linking, and defines protection as indistinguishability within a set of at least k records.
SupportedVerified against the work's full text
Sweeney shows that removing names fails when attribute combinations are unique, and ties protection to how many indistinguishable candidates remain (k).
“The greater the number of candidates provided, the more ambiguous the linking”
- Protection is the size of the surviving set, which makes concealment a quantity rather than a state, and is why deleting a name changes nothing once the intersection is a singleton.
Mechanisms¶
- Coarsening and Generalization Policy
- Its strength is that it is truthful, auditable, and intuitive: it directly implements the idea of hiding each person in a crowd of look-alikes
This sourceDefines k-anonymity as making each person indistinguishable from at least k−1 others in the released data.
- Its strength is that it is truthful, auditable, and intuitive: it directly implements the idea of hiding each person in a crowd of look-alikes
- Threshold Suppression
- The underlying principle is k-anonymity: each released record should be indistinguishable within a group of at least k, and small-cell suppression is one way to enforce it.
This sourceDefines k-anonymity so each quasi-identifier value sequence appears at least k times in released data.
- The underlying principle is k-anonymity: each released record should be indistinguishable within a group of at least k, and small-cell suppression is one way to enforce it.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 2 of 4 citations: 1 supported, 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:c19ea9ab0026 · see in the full table