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Group Privacy-aware Disclosure of Association Graph Data

Palanisamy, Balaji and Li, Chao and Krishnamurthy, Prashant (2018) Group Privacy-aware Disclosure of Association Graph Data. In: 2017 IEEE International Conference on Big Data.

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In the age of Big Data, we are witnessing a huge proliferation of digital data capturing our lives and our surroundings. Data privacy is a critical barrier to data analytics and privacy-preserving data disclosure becomes a key aspect to leveraging large-scale data analytics due to serious privacy risks. Traditional privacy-preserving data publishing solutions have focused on protecting individual's private information while considering all aggregate information about individuals as safe for disclosure. This paper presents a new privacy-aware data disclosure scheme that considers group privacy requirements of individuals in bipartite association graph datasets (e.g., graphs that represent associations between entities such as customers and products bought from a pharmacy store) where even aggregate information about groups of individuals may be sensitive and need protection. We propose the notion of εg-Group Differential Privacy that protects sensitive information of groups of individuals at various defined group protection levels, enabling data users to obtain the level of information entitled to them. Based on the notion of group privacy, we develop a suite of differentially private mechanisms that protect group privacy in bipartite association graphs at different group privacy levels based on specialization hierarchies. We evaluate our proposed techniques through extensive experiments on three real-world association graph datasets and our results demonstrate that the proposed techniques are effective, efficient and provide the required guarantees on group privacy.


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Item Type: Conference or Workshop Item (Paper)
Status: Published
CreatorsEmailPitt UsernameORCID
Date: 15 January 2018
Date Type: Publication
Journal or Publication Title: 2017 IEEE International Conference on Big Data
Publisher: IEEE
Page Range: pp. 1043-1052
Event Title: 2017 IEEE International Conference on Big Data
Event Type: Conference
DOI or Unique Handle: 10.1109/bigdata.2017.8258028
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Official URL:
Date Deposited: 14 May 2018 18:42
Last Modified: 14 May 2018 18:42


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