HyObscure: Hybrid Obscuring for Privacy-Preserving Data Publishing

Minimizing privacy leakage while ensuring data utility is a critical problem in a privacy-preserving data publishing task, from which data holders can boost platform engagements or enlarge data values. Most prior research concerned only with either privacy-insensitive or exact private data and resorts to a single obscuring method to achieve a privacy-utility tradeoff, which is inadequate for real-life hybrid data especially when facing machine learning-based inference attacks. This work takes a pilot study on privacy-preserving data publishing when both widely adopted generalization and obfuscation operations are employed for privacy-heterogeneous data protection. Specifically, we first propose novel measures for privacy and utility values quantification and formulate the hybrid privacy-preserving data obscuring problem to account for the joint effect of generalization and obfuscation. We then design a novel protection mechanism called HyObscure, which decomposes the original problem into three sub-problems to cross-iteratively optimize the hybrid operations for maximum privacy protection under a certain data utility guarantee. The convergence of the iterative process and the privacy leakage bound of HyObscure are also provided in theory. Extensive experiments demonstrate that HyObscure significantly outperforms a variety of state-of-the-art baseline methods when facing various inference attacks in different scenarios.