PSAC: Privacy-Preserving Statistical Analysis Framework for Crowdsourcing Using Histograms
Bin Zhu, Kaiping Xue, Jingcheng Zhao, Xianchao Zhang, David S. L. Wei, Qibin Sun, Jun Lu · IEEE Transactions on Dependable and Secure Computing · 2025
Crowdsourcing has emerged as an effective paradigm for large-scale data collection and statistical analysis. However, the paramount concern about worker privacy has driven the development of privacy-preserving statistical analysis methods. We propose PSAC, a novel framework that leverages histograms to facilitate privacy-preserving statistical analysis in crowdsourcing. PSAC integrates secure statistical analysis protocols based on homomorphic encryption and secure two-party computation, addressing the limitations of a single cryptographic technique. It introduces innovative algorithms using histograms for statistical operations, including functions such as quantile estimation, outlier elimination, contingency table construction for$\chi ^{2}$test, and the Mann-Whitney$U$test. These algorithms exhibit minimal overhead growth with respect to data volume, demonstrating exceptional scalability for large numbers of data. Moreover, through a key-separation design, PSAC ensures that only the requester can decrypt the final results independently, even if the ciphertexts of data are exposed. Comprehensive evaluations validate the security, efficiency, and scalability of the PSAC framework.