Autocrypt homomorphic acm5/23/2023 ![]() However, most existing solutions are based on complicated cryptographic protocol which leads to large computational costs and a heavy communication burden. proposed EPTD for mobile crowd sensing systems, in which a double-masking protocol is designed to guarantee strong privacy. proposed a cloud-enabled privacy-preserving truth discovery framework (PPTD) for crowd sensing systems that uses a homomorphic cryptosystem to guarantee both high accuracy and strong privacy. In light of the importance of the privacy issue, privacy-preserving truth discovery, which aims to estimate user quality and infer true information using quality-aware data aggregation with privacy preserved, has been extensively investigated in the recent literature,. Essentially, in most existing works, truth discovery is achieved by iteratively updating the weights of sources and further aggregating the truth value to successively approximate the ground truth,. ![]() were the first to define truth discovery for handling the data conflict problem, the goal of which is to find the most accurate description based on the conflicting information provided by different data sources. Therefore, users may deliberately upload incorrect information to protect their privacy, which makes the data conflict more serious. Furthermore, in some scenarios, sensory data may contain private and/or personal information about users. Inaccurate data, such as data that are incorrect or biased and thus not fully consistent with the ground truth, leads to conflicting descriptions of the same entity, defined as data conflicts. However, the data collected from participants might be unreliable due to sensor malfunctions, inadvertent miscalculations, or even malicious deception. Numerous crowd sensing systems have been developed to date, serving a wide range of applications, such as urban sensing, smart transportation, environmental monitoring, health care, wireless sensor networks, etc. Mobile crowd sensing has become a popular paradigm for collecting sensory data in recent years. Extensive experiments conducted on real-world datasets demonstrate the high performance of our method compared with existing mechanisms. ![]() It thus enhances the robustness of a crowd sensing system. Furthermore, DPriTD is independent of a centralized server and can perform reliably when not all participants are online in real time. ![]() Because each sensitive data point, considered to be a secret, is split into a batch of shares, and the secret cannot be recovered unless a sufficient number of shares are aggregated, DPriTD achieves effective truth discovery while protecting sensitive data from collusion attacks. DPriTD provides a strict privacy guarantee for crowd sensing applications. The proposed approach leverages the additively homomorphic property of Shamir's Secret Sharing scheme to protect user's privacy. In this paper, we propose DPriTD, a decentralized privacy-preserving framework for truth discovery in crowd sensing. Most existing studies apply a centralized architecture based on a cryptographic system, which may be vulnerable to single-point attacks and also has a very high computational cost. As the sensory data to be collected may include sensitive information about users, privacy-preserving truth discovery has attracted significant attention in recent years. Truth discovery is an efficient technique for tackling data conflict problems in crowd sensing for distributed data collection.
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