Group-wise K-anonymity meets (ϵ, δ) Differentially Privacy Scheme

Published: 29 Apr 2024, Last Modified: 30 Apr 2024Thewebconf (WWW)EveryoneCC BY 4.0
Abstract: We studied the link between K-anonymity and differential privacy as the basis for deriving a novel method for noise estimation. Hence, we provide threefold contributions: First, we use the birthday-bound paradox for uniqueness to estimate the noise level, ϵ in (ϵ, δ) differentially privacy scheme. Second, our group-aware formulation provides resilience to a series of inference attacks by using the group privacy property in our unique group-centric formulation. Third, draw a connection between the attacker advantage, δ, and ϵ for univariate and multivariate cases. Finally, we demonstrate applicability in Laplacian, Gaussian, and Exponential mechanisms
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