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L -diversity: Privacy beyond k -anonymity
Abstract
Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k -anonymity has gained popularity. In a k -anonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain identifying attributes. In this article, we show using two simple attacks that a k -anonymized dataset has some subtle but severe privacy problems. First, an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. This is a known problem. Second, attackers often have background knowledge, and we show that k -anonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks, and we propose a novel and powerful privacy criterion called ℓ-diversity that can defend against such attacks. In addition to building a formal foundation for ℓ-diversity, we show in an experimental evaluation that ℓ-diversity is practical and can be implemented efficiently.
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In the citing paper
“To address these shortcomings, Machanavajjhala et al. [162] proposed l-diversity, which ensures that each sensitive attribute of the user should have l distinct values for every equivalence class, which share the same quasi-identifier values.”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [162]
In the citing paper
“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 [205]
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Domingo‐Ferrer, J., Ricci, S., & Soria-Comas, J. (2015) · Conference on Privacy, Security and Trust · doi:10.1109/pst.2015.7232951 · Cited by 33
Metadata
- Authors
- Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M.
- Affiliations
- Cornell University (US)
- Venue
- ACM Transactions on Knowledge Discovery from Data
- Published
- 2007-03
- Volume
- 1
- Issue
- 1
- Pages
- 3
- ISSN
- 1556-4681 (ISSN-L)
- 1556-472X
- OpenAlex
- W2134167315
- Cited by
- 3,617
- References
- 72
- Citations by year
- 2026: 96 · 2025: 180 · 2024: 152 · 2023: 179 · 2022: 179 · 2021: 237 · 2020: 199 · 2019: 183 · 2018: 245 · 2017: 248 · 2016: 232 · 2015: 246 · 2014: 264 · 2013: 219 · 2012: 189
- FWCI
- 155.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Knowledge Discovery from Data (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Computer Networks and Communications › Access Control and Trust
- Keywords
- l-diversity, k-anonymity, privacy preservation, data anonymization, background knowledge attack, privacy attacks
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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