Accurately Estimating Frequencies of Relations With Relation Privacy Preserving in Decentralized Networks

Abundant valuable knowledge can be obtained by learning frequencies of relations in a decentralized network, which benefits various further complex tasks, such as range query and commodity recommendation. Nonetheless, counting frequencies in original networks can reveal sensitive data and pose a risk to individual privacy, specifically relation privacy. Current privacy notions do not fully preserve both the privacy of relations’ values and existence. In this paper, we introduce an enhanced privacy notion, relation local differential privacy (relation LDP), which provides comprehensive preservation in relation privacy. However, a significant amount of noise in perturbed networks often leads to severe errors in the accuracy of relation frequencies. To obtain accurate frequencies, we propose a framework called frequency estimation based on combination (Fest-C), which decomposes relation frequencies into two independent parts, the total frequency of relations and relative proportions. Binning relations into hyper-relations, Fest-C reduces errors of total frequency and relative proportions, respectively, and then estimates frequencies by logically multiplying them. Finally, we rigorously prove that Fest-C satisfies relation LDP. Our experiments on various datasets confirm the high accuracy of Fest-C in estimating relation frequencies with a much lower time overhead compared to competitors.