PDCS: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing

Mobile sensing mines group information through sensing and aggregating users’ data. Among major mobile sensing applications, the distinct counting problem aiming to find the number of distinct elements in a data stream with repeated elements, is extremely important for avoiding waste of resources. Besides, the privacy protection of users is also a critical issue for aggregation security. However, it is a challenge to meet these two requirements simultaneously since normal privacy-preserving methods would have negative influence on the accuracy and efficiency of distinct counting. In this paper, we propose a Privacy-preserving Distinct Counting Scheme (PDCS) for mobile sensing. By integrating the basic idea of homomorphic encryption into Flajolet-Martin (FM) sketch, PDCS allows an aggregator to conduct distinct counting over large-scale data sets without knowing privacy of users. Moreover, PDCS supports various forms of sensing data, including camera images, location data, etc. PDCS expands each bit of the hashing values of users’ original data, FM sketch is thus enhanced for encryption to protect users’ privacy. We prove the security of PDCS under known-plaintext model. The theoretic and experimental results show that PDCS achieves high counting accuracy and practical efficiency with scalability over large-scale data sets.