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Correlated differential privacy: hiding information in non-IID data set

Zhu, Tianqing, Xiong, Ping, Li, Gang and Zhou, Wanlei 2015, Correlated differential privacy: hiding information in non-IID data set, IEEE transactions on information forensics and security, vol. 10, no. 2, pp. 229-242, doi: 10.1109/TIFS.2014.2368363.

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Title Correlated differential privacy: hiding information in non-IID data set
Author(s) Zhu, TianqingORCID iD for Zhu, Tianqing orcid.org/0000-0003-3411-7947
Xiong, Ping
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Journal name IEEE transactions on information forensics and security
Volume number 10
Issue number 2
Start page 229
End page 242
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-02
ISSN 1556-6013
1556-6021
Keyword(s) privacy preserving
differential privacy
correlated dataset
non-IID dtaset
Science & Technology
Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
non-IID dataset
Summary Privacy preserving on data mining and data release has attracted an increasing research interest over a number of decades. Differential privacy is one influential privacy notion that offers a rigorous and provable privacy guarantee for data mining and data release. Existing studies on differential privacy assume that in a data set, records are sampled independently. However, in real-world applications, records in a data set are rarely independent. The relationships among records are referred to as correlated information and the data set is defined as correlated data set. A differential privacy technique performed on a correlated data set will disclose more information than expected, and this is a serious privacy violation. Although recent research was concerned with this new privacy violation, it still calls for a solid solution for the correlated data set. Moreover, how to decrease the large amount of noise incurred via differential privacy in correlated data set is yet to be explored. To fill the gap, this paper proposes an effective correlated differential privacy solution by defining the correlated sensitivity and designing a correlated data releasing mechanism. With consideration of the correlated levels between records, the proposed correlated sensitivity can significantly decrease the noise compared with traditional global sensitivity. The correlated data releasing mechanism correlated iteration mechanism is designed based on an iterative method to answer a large number of queries. Compared with the traditional method, the proposed correlated differential privacy solution enhances the privacy guarantee for a correlated data set with less accuracy cost. Experimental results show that the proposed solution outperforms traditional differential privacy in terms of mean square error on large group of queries. This also suggests the correlated differential privacy can successfully retain the utility while preserving the privacy.
Language eng
DOI 10.1109/TIFS.2014.2368363
Field of Research 080109 Pattern Recognition and Data Mining
08 Information And Computing Sciences
09 Engineering
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076106

Document type: Journal Article
Collection: School of Information Technology
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