Semantic Noise: Privacy-protection of Nominal Microdata through Uncorrelated Noise Addition

Personal data are of great interest in statistical studies and to provide personalized services, but its release may impair the privacy of individuals. To protect the privacy, in this paper, we present the notion and practical enforcement of semantic noise, a semantically-grounded version of the numerical uncorrelated noise addition method, which is capable of masking textual data while properly preserving their semantics. Unlike other perturbative masking schemes, our method can work with both datasets containing information of several individuals and single data. Empirical results show that our proposal provides semantically-coherent outcomes preserving data utility better than non-semantic perturbative mechanisms.