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Group Differential Privacy-Preserving Disclosure of Multi-level Association Graphs

Palanisamy, B and Li, C and Krishnamurthy, P (2017) Group Differential Privacy-Preserving Disclosure of Multi-level Association Graphs. In: UNSPECIFIED.

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Abstract

© 2017 IEEE. Traditional privacy-preserving data disclosure solutions have focused on protecting the privacy of individual's information with the assumption that all aggregate (statistical) information about individuals is safe for disclosure. Such schemes fail to support group privacy where aggregate information about a group of individuals may also be sensitive and users of the published data may have different levels of access privileges entitled to them. We propose the notion of Eg-Group Differential Privacy that protects sensitive information of groups of individuals at various defined privacy levels, enabling data users to obtain the level of access entitled to them. We present a preliminary evaluation of the proposed notion of group privacy through experiments on real association graph data that demonstrate the guarantees on group privacy on the disclosed data.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Palanisamy, B
Li, Cchl205@pitt.eduCHL205
Krishnamurthy, P
Date: 13 July 2017
Date Type: Publication
Journal or Publication Title: Proceedings - International Conference on Distributed Computing Systems
Page Range: 2587 - 2588
Event Type: Conference
DOI or Unique Handle: 10.1109/icdcs.2017.223
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9781538617915
Date Deposited: 14 Jul 2017 16:31
Last Modified: 13 Oct 2017 21:58
URI: http://d-scholarship.pitt.edu/id/eprint/32724

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