An Enhanced K-Anonymity Model against Homogeneity Attack

@article{Wang2011AnEK,
  title={An Enhanced K-Anonymity Model against Homogeneity Attack},
  author={Qian Wang and Zhiwei Xu and Shengzhi Qu},
  journal={J. Softw.},
  year={2011},
  volume={6},
  pages={1945-1952}
}
k-anonymity is an important model in the field of privacy protection and it is an effective method to prevent privacy disclosure in micro-data release. However, it is ineffective for the attribute disclosure by the homogeneity attack. The existing models based on k-anonymity have solved this problem to a certain extent, but they did not distinguish the different values of the sensitive attribute, processed a series of unnecessary generalization and expanded the information loss when they… 

Figures and Tables from this paper

A New Method for Preserving Privacy in Data Publishing
TLDR
A new method for preserving the privacy of individuals’ sensitive information from attribute and identity disclosure attacks is contributed and privacy preservation is full filled through generalization of quasi identifiers by setting range values.
A Review on anonymization approach to preserve privacy of Published data through record elimination
TLDR
In the proposed method, privacy preservation is achieved through generalization by setting range values and through record elimination and overcomes the drawback of both record linkage attack and attribute linkage attack.
Anonymization technique through record elimination to preserve privacy of published data
  • R. Mahesh, T. Meyyappan
  • Computer Science
    2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering
  • 2013
TLDR
A new method to preserve the privacy of individuals' sensitive data from record and attribute linkage attacks is proposed through generalization of quasi identifier by setting range values and record elimination.
Privacy Preservation of Published Data Using Anonymization Technique
TLDR
A proposed method used to preserve the privacy of person sensitive data from record and attribute linkage attacks is achieved through generalization by setting range values and through record elimination of duplicate data.
Anonymizied Approach to Preserve Privacy of Published Data Through Record Elimination
TLDR
In the proposed method, privacy preservation is achieved through generalization by setting range values and through record elimination, which overcomes the drawback of both record linkage attack and attribute linkage attack.
Correlation Based Anonymization Using Generalization and Suppression for Disclosure Problems
Huge volume of detailed personal data is regularly collected and sharing of these data is proved to be beneficial for data mining application. Data that include shopping habits, criminal records,
A Reliable Method for Accuracy Constrained Privacy Preservation for Relational Data
TLDR
To preserve the privacy of individuals’ sensitive information from attribute and identity disclosure attacks a new method is proposed and minimization information loss and gains the privacy by using generalization algorithm which is proposed in this method and is described in this paper.
Every Anonymization Begins with k: A Game-Theoretic Approach for Optimized k Selection in k-Anonymization
TLDR
The problem of choosing the optimal anonymization level for k-anonymization, under possible attacks, is studied when multiple organizations share their data to a common platform and a novel game-theoretic framework is proposed to model the interactions between the sharing organizations and the attacker.
Finding the Sweet Spot for Data Anonymization: A Mechanism Design Perspective
TLDR
A two-tier mathematical framework is proposed for analyzing and mitigating the de-anonymization attacks, by studying the interactions between sharing organizations, data collector, and a prospective attacker.
Efficient Technique for Annonymized Microdata Preservation using Slicing
TLDR
An effective method that can be used for providing better data utility and can handle high-dimensional data is presented, known as Slicing.
...
...

References

SHOWING 1-10 OF 15 REFERENCES
(p+, α)-sensitive k-anonymity: A new enhanced privacy protection model
TLDR
A new privacy protection model called (p+, alpha)-sensitive k-anonymity is proposed, where sensitive attributes are first partitioned into categories by their sensitivity, and then the categories that sensitive attributes belong to are published.
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
TLDR
It is proved that the optimal (α, k)-anonymity problem is NP-hard, and a local-recoding algorithm is proposed which is more scalable and result in less data distortion.
(α, β, k)-anonymity: An effective privacy preserving model for databases
TLDR
It is verified that (α, β, k)-anonymity approach can effectively protect privacy information of individual and resist background knowledge attack in publishing the data with multiple sensitive attributes by specific example.
(a, d)-Diversity: Privacy Protection Based on l-Diversity
TLDR
This paper analyzes the cause of attribute disclosure and proposes a novel idea for privacy protection based on l-Diversity that takes the semantic meaning of the sensitive attributes into consideration and gives a stronger definition of privacy protection.
Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining
Preservation of proximity privacy in publishing numerical sensitive data
TLDR
This work identifies proximity breach as a privacy threat specific to numerical sensitive attributes in anonymized data publication and introduces a novel principle called (ε, m)-anonymity, which demands that, given a QI-group G, for every sensitive value x in G, at most 1/m of the tuples in G can have sensitive values "similar" to x, where the similarity is controlled by ε.
Achieving k-Anonymity Privacy Protection Using Generalization and Suppression
  • L. Sweeney
  • Computer Science
    Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 2002
TLDR
This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity and shows that Datafly can over distort data and µ-Argus can additionally fail to provide adequate protection.
k-Anonymity: A Model for Protecting Privacy
  • L. Sweeney
  • Computer Science
    Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 2002
TLDR
The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment and examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected.
Privacy Protection: p-Sensitive k-Anonymity Property
  • T. Truta, B. Vinay
  • Computer Science
    22nd International Conference on Data Engineering Workshops (ICDEW'06)
  • 2006
TLDR
Two necessary conditions to achieve p-sensitive kanonymity property are presented, and used in developing algorithms to create masked microdata with p- sensitive k-anonymityproperty using generalization and suppression.
Privacy Preserving k-Anonymity for Re-publication of Incremental Datasets
TLDR
A partitioning based algorithm is proposed that can securely anonymize a continuously growing dataset in an efficient manner while assuring high data quality and effectively prevents privacy breach in re-publication.
...
...