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EDBT
2016
ACM

Using Randomized Response for Differential Privacy Preserving Data Collection

3 years 18 days ago
Using Randomized Response for Differential Privacy Preserving Data Collection
This paper studies how to enforce differential privacy by using the randomized response in the data collection scenario. Given a client’s value, the randomized algorithm executed by the client reports to the untrusted server a perturbed value. The use of randomized response in surveys enables easy estimations of accurate population statistics while preserving the privacy of the individual respondents. We compare the randomized response with the standard Laplace mechanism which is based on query-output independent adding of Laplace noise. Our research starts from the simple case with one single binary attribute and extends to the general case with multiple polychotomous attributes. We measure utility preservation in terms of the mean squared error of the estimate for various calculations including individual value estimate, proportion estimate, and various derived statistics. We theoretically derive the explicit formula of the mean squared error of various derived statistics based on...
Yue Wang 0009, Xintao Wu, Donghui Hu
Added 02 Apr 2016
Updated 02 Apr 2016
Type Journal
Year 2016
Where EDBT
Authors Yue Wang 0009, Xintao Wu, Donghui Hu
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