An Efficient Two-Layer Mechanism for Privacy-Preserving Truth Discovery
An Efficient Two-Layer Mechanism for Privacy-Preserving Truth Discovery
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DOI：
10.1145/3219819.3219998
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Yaliang Li;Chenglin Miao;Lu Su;Jing Gao;Qi Li;Bolin Ding;Zhan Qin;K. Ren
中科院分区：
文献类型：
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作者：
Yaliang Li;Chenglin Miao;Lu Su;Jing Gao;Qi Li;Bolin Ding;Zhan Qin;K. Ren
向在线用户征求答案是解决许多具有挑战性的任务的高效和有效的解决方案。由于用户质量的多样性，推断他们在聚合期间提供正确答案的能力是很重要的。因此，可以使用真相发现方法来自动捕获用户质量，并通过加权组合来聚合用户贡献的答案。尽管事实真相发现是答案聚合的有效工具，但现有的工作福尔斯缺乏对参与用户隐私的保护。为了填补这一空白，我们提出了基于扰动的机制，为用户提供隐私保证，并保持聚合答案的准确性。首先，我们提出了一个单层机制，其中所有的用户采用相同的概率扰动他们的答案。然后对扰动的答案进行聚合，但聚合准确性可能会相应下降。为了提高效用，提出了一种两层机制，允许用户从超分布中采样自己的概率。我们从理论上比较了一层和两层机制，并证明了他们提供了相同的隐私保证，而两层机制提供了更好的效用。这一优势是由于两层机制可以利用真实发现中估计的用户质量信息来减少扰动造成的准确性损失，这一点在真实世界数据集上的实验结果得到了证实。实验结果也证明了所提出的两层机制在隐私保护的有效性与容忍的准确性损失的聚集。
Soliciting answers from online users is an efficient and effective solution to many challenging tasks. Due to the variety in the quality of users, it is important to infer their ability to provide correct answers during aggregation. Therefore, truth discovery methods can be used to automatically capture the user quality and aggregate user-contributed answers via a weighted combination. Despite the fact that truth discovery is an effective tool for answer aggregation, existing work falls short of the protection towards the privacy of participating users. To fill this gap, we propose perturbation-based mechanisms that provide users with privacy guarantees and maintain the accuracy of aggregated answers. We first present a one-layer mechanism, in which all the users adopt the same probability to perturb their answers. Aggregation is then conducted on perturbed answers but the aggregation accuracy could drop accordingly. To improve the utility, a two-layer mechanism is proposed where users are allowed to sample their own probabilities from a hyper distribution. We theoretically compare the one-layer and two-layer mechanisms, and prove that they provide the same privacy guarantee while the two-layer mechanism delivers better utility. This advantage is brought by the fact that the two-layer mechanism can utilize the estimated user quality information from truth discovery to reduce the accuracy loss caused by perturbation, which is confirmed by experimental results on real-world datasets. Experimental results also demonstrate the effectiveness of the proposed two-layer mechanism in privacy protection with tolerable accuracy loss in aggregation.