Survey on Improving Data Utility in Differentially Private Sequential Data Publishing - 2017

PROJECT TITLE :

Survey on Improving Data Utility in Differentially Private Sequential Data Publishing - 2017

ABSTRACT:

The large generation, intensive sharing, and deep exploitation of knowledge in the massive knowledge era have raised unprecedented privacy threats. To address privacy concerns, varied privacy paradigms are proposed to achieve a sensible tradeoff between privacy and data utility. Particularly, differential privacy has been well accepted together of the de facto standards for privacy preservation, and various schemes guaranteeing differential privacy have been proposed. Nonetheless, most of the prevailing works claiming a superior utility-privacy tradeoff only present specific methods, with distinct perspectives, and an entire comparative analysis and evaluation study has not been absolutely investigated. To this end, in this paper we have a tendency to review and investigate existing schemes on providing differential privacy from a broad and encompassing perspective to provide a comprehensive survey with respect to both the privacy guarantee and also the effectiveness and potency in utility improvement. We tend to categorize the prevailing schemes into distribution optimization, sensitivity calibration, transformation, decomposition, and correlations exploitation, primarily based on their mechanisms in improving knowledge utility. We have a tendency to additionally conduct some analysis and comparison of their various ideas and principles, specializing in enhancements to knowledge utility. Finally, we tend to outline some challenges and offer future research directions.

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