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Title: Sensitive Disclosures under Differential Privacy Guarantees

Citation Type: Miscellaneous

Publication Year: 2015

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Abstract: Non-independent reasoning (NIR) refers to learning the information of one record from other records, under the assumption that these records share the same underlying distribution. Accurate NIR could disclose private information of an individual. An important assumption made by differential privacy is that NIR is considered to be non-violation of privacy. In this work, we investigate the extent to which private information of an individual may be disclosed through NIR by query answers that satisfy differential privacy. We first define what a disclosure means under NIR by randomized query answers. We then present a formal analysis on such disclosures by differentially private query answers. Our analysis on real life datasets demonstrates that while disclosures of NIR can be eliminated by adopting a more restricted setting of differential privacy, such settings adversely affects the utility of query answers for data analysis, and this conflict cannot be easily solved because both disclosures and utility depend on the accuracy of noisy query answers. This study suggests that under the assumption that the disclosure through NIR is a privacy concern, differential privacy is not suitable because it does not provide both privacy and utility.

Url: https://www.cs.sfu.ca/~wangk/pub/BigData2015.pdf

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Authors: Han, Chao; Wang, Ke

Publisher: Simon Fraser University

Data Collections: IPUMS USA

Topics: Other

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