Truth discovery is an effective way to identify the aggregated truth of each task among multiple observed data drawn from different workers of varying reliabilities. However, existing studies are insufficient to protect individuals’ privacy, as they either just guarantee the weaker versions of local differential privacy (LDP) or potentially assume that the tasks are independent. In this paper, we, for the first time, investigate the problem of truth discovery while achieving the rigorous LDP for each worker with continuous inputs without the independence assumption. We present a locally differentially private truth discovery approach calledPrivTDSIbased on sampling and inference with solid privacy and utility guarantees. InPrivTDSI, the server first determines which values of each worker should be sampled according to a sample proportion and sends the indexes of these values to each worker. Then, each worker adds noise into the sampled values for privacy protection and uploads them to the server. After receiving the noisy sampled values from all the workers, the server first infers the unsampled values and then conducts truth discovery based on both the noisy sampled values and the inferred values. In particular, to determine the sample proportion, we formulate aconstrained nonlinear programmingproblem and give a closed-form solution to this problem. Moreover, to determine which values of each worker should be sampled while avoiding the situation where the values of some workers or tasks might not be sampled at all, we develop a two-stage sampling method calledTOSS. Furthermore, to infer the unsampled values accurately, we design a quality-aware inference method based on matrix factorization calledQualityMF. Experimental results on two real-world datasets and a synthetic dataset demonstrate the effectiveness of${PrivTDSI}$.

In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn N. The core of DP-SUBN is the construction of the search frontier, which can be seen as a priori knowledge to guide the parties to update N. By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the search frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies ε-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.