PSAC: Privacy-Preserving Statistical Analysis Framework for Crowdsourcing Using Histograms

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\chi ^{2}$</tex-math></inline-formula> test, and the Mann-Whitney <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$U$</tex-math></inline-formula> 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.