Ziqi Yan

Ziqi Yan, Gang Li, Jiqiang Liu

As a method for answer aggregation in crowdsourced data management, rank aggregation aims to combine different agents' answers or preferences over the given alternatives into an aggregate ranking which agrees the most with the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data could be compromised when the curator is untrusted. Existing works that guarantee differential privacy in rank aggregation all assume that the data curator is trusted. In this paper, we formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator. By leveraging the approximate rank aggregation algorithm KwikSort, the Randomized Response mechanism, and the Laplace mechanism, we propose an effective and efficient protocol LDP-KwikSort. Theoretical and empirical results show that the solution LDP-KwikSort:RR can achieve the acceptable trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.

Ziqi Yan, Jiqiang Liu, Gang Li et al.

In many industrial applications of big data, the Jaccard Similarity Computation has been widely used to measure the distance between two profiles or sets respectively owned by two users. Yet, one semi-honest user with unpredictable knowledge may also deduce the private or sensitive information (e.g., the existence of a single element in the original sets) of the other user via the shared similarity. In this paper, we aim at solving the privacy issues in Jaccard similarity computation with strict differential privacy guarantees. To achieve this, we first define the Conditional $ε$-DPSO, a relaxed differential privacy definition regarding set operations, and prove that the MinHash-based Jaccard Similarity Computation (MH-JSC) satisfies this definition. Then for achieving strict differential privacy in MH-JSC, we propose the PrivMin algorithm, which consists of two private operations: 1) the Private MinHash Value Generation that works by introducing the Exponential noise to the generation of MinHash signature. 2) the Randomized MinHashing Steps Selection that works by adopting Randomized Response technique to privately select several steps within the MinHashing phase that are deployed with the Exponential mechanism. Experiments on real datasets demonstrate that the proposed PrivMin algorithm can successfully retain the utility of the computed similarity while preserving privacy.