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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Machanavajjhala et al. 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.
“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]
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 [205]
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“iduals [65,66]. However, since k-anonymity does not limit statistical inferences drawn indirectly, from subgroup intersections or differences, it cannot offer mathematically-strong privacy guarantees [32,48]. In the last decade, the definitions of differential privacy have become the accepted standard for strong privac…”
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Privacy-Preserving Data Mining: Methods, Metrics, and Applications
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Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey
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Privacy in the Smart City—Applications, Technologies, Challenges, and Solutions
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Privacy and Data Protection by Design - from policy to engineeringPreprint
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Minimality Attack in Privacy Preserving Data Publishing
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Large-Scale Mobile Traffic Analysis: A Survey
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Privacy-preserving trajectory data publishing by local suppression
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Privacy–Enhancing Face Biometrics: A Comprehensive Survey
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- 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
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Keywords
- l-diversity, k-anonymity, privacy preservation, data anonymization, background knowledge attack, privacy attacks
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