Preserving Data Privacy in Speech Data Publishing

Speech data publishing breaches users'data privacy,thereby causing more privacy disclosure.Existing work sanitizes content,voice,and voiceprint of speech data without considering the consistence among these three features,and thus is susceptible to inference attacks.To address the problem,we design a privacy-preserving protocol for speech data publishing(P3S2)that takes the corrections among the three factors into consideration.To concrete,we first propose a three-dimensional sanitization that uses feature learning to capture characteristics in each dimension,and then sanitize speech data using the learned features.As a result,the correlations among the three dimensions of the sanitized speech data are guaranteed.Furthermore,the(ε,δ)-differential privacy is used to theoretically prove both the data privacy preservation and the data utility guarantee of P3S2,filling the gap of algorithm design and performance evaluation.Finally,simulations on two real world datasets have demonstrated both the data privacy preservation and the data utility guarantee.