L-diversity: privacy beyond k-anonymity

@article{Machanavajjhala2006LdiversityPB,
  title={L-diversity: privacy beyond k-anonymity},
  author={Ashwin Machanavajjhala and Daniel Kifer and Johannes Gehrke and Muthuramakrishnan Venkitasubramaniam},
  journal={22nd International Conference on Data Engineering (ICDE'06)},
  year={2006},
  pages={24-24}
}
Publishing data about individuals without revealing sensitive information about them is an important problem. [...] Key Result In addition to building a formal foundation for \ell-diversity, we show in an experimental evaluation that \ell-diversity is practical and can be implemented efficiently.Expand
l-Diversity: Privacy Beyond k-Anonymity
TLDR
This paper shows with two simple attacks that a \kappa-anonymized dataset has some subtle, but severe privacy problems, and proposes a novel and powerful privacy definition called \ell-diversity, which is practical and can be implemented efficiently.
Composition attacks and auxiliary information in data privacy
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This paper investigates composition attacks, in which an adversary uses independent anonymized releases to breach privacy, and provides a precise formulation of this property, and proves that an important class of relaxations of differential privacy also satisfy the property.
A new perspective of privacy protection: Unique distinct l-SR diversity
TLDR
A new model, Unique Distinct l-SR diversity based on the sensitivity of private information is proposed, which achieved better performance on minimizing inference of sensitive information and reached the comparable generalization data quality compared with other data publishing algorithms.
On Minimality Attack for Privacy-Preserving Data Publishing
TLDR
An analysis of some well-known anonymization-based privacy preserving schemes such as k-anonymity and l-diversity to show how these schemes suffer from the minimality attack that can lead to potential information leakage from the published data is presented.
Fast Data Anonymization with Low Information Loss
TLDR
This paper focuses on one-dimensional (i.e., single attribute) quasi-identifiers, and study the properties of optimal solutions for k-anonymity and l-diversity, and develops efficient heuristics to solve the one- dimensional problems in linear time based on meaningful information loss metrics.
Semantic diversity: Privacy considering distance between values of sensitive attribute
TLDR
To solve how actual diversity cannot be taken into consideration with existing l-diversity, a novel privacy indicator, (l, d)-semantic diversity, and an algorithm that anonymizes a database to satisfy ( l, d-semantic Diversity) are proposed.
Enhanced p-Sensitive k-Anonymity Models for Achieving Better Privacy
TLDR
Two enhanced anonymous models with personalized protection characteristic, that is, (p, αisg) -sensitive k-anonymity model and (pi, α isg)-sensitivek-anonymsity model, are proposed to resist skew attacks and sensitive attacks and show outstanding advantages in better privacy at the expense of a little data utility.
A robust privacy preserving model for data publishing
TLDR
This paper is proposing a new privacy preserving model, which minimizes attacks and overcomes drawbacks experienced by existing popular anonymizing approaches, and includes features combining these two techniques which lead to an efficient model.
Flexible Anonymization For Privacy Preserving Data Publishing: A Systematic Search Based Approach
TLDR
A systematic enumeration based branchand-bound technique that explores a much richer space of solutions than any previous method in literature and further enhances the basic algorithm to incorporate heuristics that potentially accelerate the search process significantly.
A framework for efficient data anonymization under privacy and accuracy constraints
TLDR
This article focuses on one-dimensional (i.e., single-attribute) quasi-identifiers, and study the properties of optimal solutions under the k-anonymity and l-diversity models for the privacy-constrained and the accuracy- Constrained anonymization problems.
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