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Reversible Data Perturbation Techniques for Multi-level Privacy-preserving Data Publication

Li, Chao and Palanisamy, Balaji and Krishnamurthy, Prashant (2018) Reversible Data Perturbation Techniques for Multi-level Privacy-preserving Data Publication. In: 7th International Congress on Big Data. (In Press)

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The amount of digital data generated in the Big Data age is increasingly rapidly. Privacy-preserving data publishing techniques based on differential privacy through data perturbation provide a safe release of datasets such that sensitive information present in the dataset cannot be inferred from the published data. Existing privacy-preserving data publishing solutions have focused on publishing a single snapshot of the data with the assumption that all users of the data share the same level of privilege and access the data with a fixed privacy level. Thus, such schemes do not directly support data release in cases when data users have different levels of access on the published data. While a straight-forward approach of releasing a separate snapshot of the data for each possible data access level can allow multi-level access, it can result in a higher storage cost requiring separate storage space for each instance of the published data. In this paper, we develop a set of reversible data perturbation techniques for large bipartite association graphs that use perturbation keys to control the sequential generation of multiple snapshots of the data to offer multi-level access based on privacy levels. The proposed schemes enable multi-level data privacy, allowing selective de-perturbation of the published data when suitable access credentials are provided. We evaluate the techniques through extensive experiments on a large real-world association graph dataset and our experiments show that the proposed techniques are efficient, scalable and effectively support multi-level data privacy on the published data.


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Item Type: Conference or Workshop Item (Paper)
Status: In Press
CreatorsEmailPitt UsernameORCID
Date: 2 May 2018
Date Type: Acceptance
Event Title: 7th International Congress on Big Data
Event Type: Conference
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Date Deposited: 14 May 2018 18:37
Last Modified: 14 May 2018 18:37


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