HyObscure: Hybrid Obscuring for Privacy-Preserving Data Publishing
Xiao Han, Yuncong Yang, Junjie Wu

TL;DR
HyObscure introduces a hybrid data obscuring method combining generalization and obfuscation to enhance privacy protection for heterogeneous data while maintaining utility, addressing limitations of prior single-method approaches.
Contribution
This work proposes a novel hybrid privacy-preserving mechanism, HyObscure, optimizing combined generalization and obfuscation for heterogeneous data with theoretical guarantees.
Findings
HyObscure outperforms state-of-the-art methods against inference attacks.
It scales linearly with data size and maintains robustness across parameters.
Theoretical convergence and privacy bounds are established.
Abstract
Minimizing privacy leakage while ensuring data utility is a critical problem to data holders in a privacy-preserving data publishing task. Most prior research concerns only with one type of data and resorts to a single obscuring method, \eg, obfuscation or generalization, to achieve a privacy-utility tradeoff, which is inadequate for protecting real-life heterogeneous data and is hard to defend ever-growing machine learning based inference attacks. This work takes a pilot study on privacy-preserving data publishing when both generalization and obfuscation operations are employed for heterogeneous data protection. To this end, we first propose novel measures for privacy and utility 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 hybrid protection mechanism called…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Stochastic Gradient Optimization Techniques · Cryptography and Data Security
