Catch You If You Deceive Me: Verifiable and Privacy-Aware Truth Discovery in Crowdsensing Systems

Abstract

Truth Discovery (TD) is to infer truthful information by estimating the reliability of users in crowdsensing systems. To protect data privacy, many Privacy-Preserving Truth Discovery (PPTD) approaches have been proposed. However, all existing PPTD solutions do not consider a fundamental issue of trust. That is, if the data aggregator (e.g., the cloud server) is not trustworthy, how can an entity be convinced that the data aggregator has correctly performed the PPTD? A "lazy" cloud server may partially follow the deployed protocols to save its computing and communication resources, or worse, maliciously forge the results for some shady deals. In this paper, we propose V-PATD, the first Verifiable and Privacy-Aware Truth Discovery protocol in crowdsensing systems. In V-PATD, a publicly verifiable approach is designed enabling any entity to verify the correctness of aggregated results returned from the server. Since most of the computation burdens are carried by the cloud server, our verification approach is efficient and scalable. Moreover, users' data is perturbed with the principles of local differential privacy. Security analysis shows that the proposed perturbation mechanism guarantees a high aggregation accuracy even if large noises are added. Compared to existing solutions, extensive experiments conducted on real crowdsensing systems demonstrate the superior performance of V-PATD in terms of accuracy, computation and communication overheads.

Advances in relationship between cell senescence and atherosclerosis

“…The outcomes demonstrate that our scheme's efficiency is 2 − 4× higher than that of recent work [8]. For a dataset comprising a large volume of labels, the algorithm's result can be proven within 20 seconds, incurring minimal verification and communication overhead.…”

“…The research mainly focuses on the accuracy of the algorithm and expanding the scenarios in which the algorithm can be applied. In recent years, many works have begun to focus on the privacy and security aspects of the algorithms [8], [17], [18], [19], [20], [21], [22]. Some works are dedicated to ensuring high accuracy of the algorithms while not revealing the workers' privacy (i.e., their answers), which is called privacy-preserving.…”

“…In section 6.3 we also discussed how to incorporate some privacyfocused methods into our framework. In existing works, [8] is the closest work related to our objective. They use a pairing-based method to convert the necessary computations into a verifiable process.…”

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“…Xu et al [112] presented the first verifiable and PPTD protocol in CS systems named V-PATD. Their openly verifiable approach lets any entity verify whether the aggregated truths returned from the cloud server are correct.…”

LensCap

“…Further verification would require certification that the cloud service only computes/stores the expected user data but does not send information to other untrusted third-party entities. To do so, LensCap could integrate with other works that protect user data against malicious cloud services and secure content sharing in multi-user collaborative AR apps [51,63]. SAFE [60], as an example, equips a set of modules including an OS, a runtime, and proxy to enforce user policies in cloud services such that user data can only be released to another SAFE system or a system allowed by SAFE policies.…”