An Efficient and Privacy-Preserving Outsourced Heavy Hitter Query Scheme with LDP Technique
Ellen Z. Zhang, Yunguo Guan, Songnian Zhang, Rongxing Lu, Harry Zhang · 2023
The rise of the Internet of Things (IoT) and Big Data technologies has led to the widespread use of crowdsourcing in data-oriented applications. However, privacy concerns remain a significant issue in crowdsourcing. In this paper, we propose an efficient and privacy-preserving outsourced heavy hitter query scheme in crowdsourcing scenarios. The proposed scheme is characterized by employing a communication efficient S-Hist Local Differential Privacy (LDP) protocol and Paillier homomorphic encryption to achieve top-k items query. Detailed security analysis shows it can achieve not only participant user’s privacy but also query result privacy. In addition, extensive performance evaluations are conducted, and the results demonstrate its efficiency, and show it can balance the query accuracy and privacy well.