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Open AccessArticle

Privacy-Preserving Monotonicity of Differential Privacy Mechanisms

1
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
2
Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
3
School of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2018, 8(11), 2081; https://doi.org/10.3390/app8112081
Received: 3 September 2018 / Revised: 16 October 2018 / Accepted: 25 October 2018 / Published: 28 October 2018
(This article belongs to the Special Issue Security and Privacy for Cyber Physical Systems)
Differential privacy mechanisms can offer a trade-off between privacy and utility by using privacy metrics and utility metrics. The trade-off of differential privacy shows that one thing increases and another decreases in terms of privacy metrics and utility metrics. However, there is no unified trade-off measurement of differential privacy mechanisms. To this end, we proposed the definition of privacy-preserving monotonicity of differential privacy, which measured the trade-off between privacy and utility. First, to formulate the trade-off, we presented the definition of privacy-preserving monotonicity based on computational indistinguishability. Second, building on privacy metrics of the expected estimation error and entropy, we theoretically and numerically showed privacy-preserving monotonicity of Laplace mechanism, Gaussian mechanism, exponential mechanism, and randomized response mechanism. In addition, we also theoretically and numerically analyzed the utility monotonicity of these several differential privacy mechanisms based on utility metrics of modulus of characteristic function and variant of normalized entropy. Third, according to the privacy-preserving monotonicity of differential privacy, we presented a method to seek trade-off under a semi-honest model and analyzed a unilateral trade-off under a rational model. Therefore, privacy-preserving monotonicity can be used as a criterion to evaluate the trade-off between privacy and utility in differential privacy mechanisms under the semi-honest model. However, privacy-preserving monotonicity results in a unilateral trade-off of the rational model, which can lead to severe consequences. View Full-Text
Keywords: differential privacy; trade-off; privacy-preserving monotonicity; semi-honest model; rational model differential privacy; trade-off; privacy-preserving monotonicity; semi-honest model; rational model
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Liu, H.; Wu, Z.; Zhou, Y.; Peng, C.; Tian, F.; Lu, L. Privacy-Preserving Monotonicity of Differential Privacy Mechanisms. Appl. Sci. 2018, 8, 2081.

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