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

Li, Chao and Palanisamy, Balaji (2017) Group Differential Privacy-preserving Disclosure of Multi-level Association Graphs. In: 37th IEEE International Conference on Distributed Computing Systems (ICDCS 2017), 5-8 Jun 2017, Atlanta, GA, USA.

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Abstract

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 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 (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Li, Chaochl205@pitt.educhl205
Palanisamy, Balajibpalan@pitt.edu
Date: 2017
Event Title: 37th IEEE International Conference on Distributed Computing Systems (ICDCS 2017)
Event Dates: 5-8 Jun 2017
Event Type: Conference
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 14 Jul 2017 16:31
Last Modified: 14 Jul 2017 16:31
URI: http://d-scholarship.pitt.edu/id/eprint/32724

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