Privacy-Preserving Collaborative Queries in Services Computing Using Multisource Private Data Counting

Multisource Private Data Counting (PDC) as a collaborative query service allows different organizations or individuals to combine their data and perform various queries without revealing sensitive information. It is especially crucial for multiple competing institutions, having economic interests and holding sensitive business information. To do it, we first design a practical privacy-preserving query service framework to meet the requirements of data and query privacy, computation fairness, and query flexibility. On this basis, we present a new PDC method over Finite Support Polynomials with Integer Coefficients (PDC-FSP-IC), in which curve fitting method is adopted to generate a query curve for given data set and target set. Especially, the symmetry of curve and Peak-Shift method are introduced to increase the flexibility and applicability for constructing query curves. By integrating PDC-FSP-IC with Multi-Party Fully Homomorphic Encryption (MP-FHE), we further present an efficient PDC scheme to perform collaborative query services on multisource data. This scheme is proved to be statistically secure against chosen element attack for both data privacy and query privacy. Furthermore, the scheme is applied into Private Blacklist-drived Credit Assessment (PBCA) and Privacy-Preserving ID3 (PP-ID3) to preserve data privacy of all participants in joint counting process. The results of performance evaluation demonstrate that our scheme is enough efficient for collaborative query services.