Privacy-Preserving Monotonicity of Differential Privacy Mechanisms

Abstract

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.

Readmission rate prediction using differential privacy technology and multilayer perceptron

“…This approach establishes a system for handling low-sensitivity queries and balances privacy protection with data analysis by controlling cumulative privacy loss. Liu et al proposed the definition of privacy-preserving monotonicity of differential privacy and used privacy metrics and utility metrics to measure the trade-off between privacy and utility in differential privacy mechanisms [16]. They defined privacy-preserving monotonicity based on computational indistinguishability and employed both privacy and utility metrics to analyze trade-off in various mechanisms.…”

B-DP: Dynamic Collection and Publishing of Continuous Check-In Data with Best-Effort Differential Privacy

“…In general, there is a contradiction between privacy and utility and it is necessary to be a trade-off [18,19]. In [19], the authors discussed a monotone trade-off in the semi-honest model.…”

“…In general, there is a contradiction between privacy and utility and it is necessary to be a trade-off [18,19]. In [19], the authors discussed a monotone trade-off in the semi-honest model. Therein, when the utility becomes worse, the privacy protection becomes stronger, and on the other hand, when the utility gets better, the privacy protection gets weaker.…”