A privacy measurement model for privacy-preserving stream histogram publishing
Fan Huanyu, Qing Ye, Chao Shan · International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022) · 2022
Privacy protection in shared data stream publishing witness its thriving in recent years. Different privacy preserving methods commonly provide different protection effects. Unified measurement of protection intensity is the basis of privacy evaluation. Existing privacy measurement methods depend too much on background knowledge, and the measurement effect is strongly related to privacy information. They in common fall short in privacy measurement in privacy-preserving stream histogram publishing. Concerning these issues, a Bayesian-theorem based privacy measurement model is proposed for privacy-preserving stream histogram publishing. By analyzing the correlation degree between background knowledge and sliding window, the correlation between background knowledge and publishing results is established. The concept of correlation histogram is introduced to analyze the correlation between histograms containing the same user's state information, and a sliding window-oriented privacy leakage measurement mechanism of correlation histograms is proposed to measure the degree of privacy leakage between correlative histograms. Further, corresponding weights are set for both error probability of attacks and privacy leakage extent, to realize privacy measurement of privacy-preserving histogram publishing algorithm. Theoretical analysis and experimental results show that our solution can effectively measure the protection strength of streaming histogram privacy protection method.