Nowadays, an increasing number of applications exploit users who act as intelligent sensors and can quickly provide high-level information. These users generate valuable data that, if mishandled, could potentially reveal sensitive information. Protecting user privacy is thus of paramount importance for crowdsensing systems. In this paper, we propose BLIND, an innovative open-source truth discovery system designed to improve the quality of information (QoI) through the use of privacy-preserving computation techniques in mobile crowdsensing scenarios. The uniqueness of BLIND lies in its ability to preserve user privacy by ensuring that none of the parties involved are able to identify the source of the information provided. The system uses homomorphic encryption to implement a novel privacy-preserving version of the well-known K-Means clustering algorithm, which directly groups encrypted user data. Outliers are then removed privately without revealing any useful information to the parties involved. We extensively evaluate the proposed system for both server-side and client-side scalability, as well as truth discovery accuracy, using a real-world dataset and a synthetic one, to test the system under challenging conditions. Comparisons with four state-of-the-art approaches show that BLIND optimizes QoI by effectively mitigating the impact of four different security attacks, with higher accuracy and lower communication overhead than its competitors. With the optimizations proposed in this paper, BLIND is up to three times faster than the baseline system, and the obtained Root Mean Squared Error (RMSE) values are up to 42% lower than other state-of-the-art approaches.

如今，越来越多的应用程序利用充当智能传感器并可以快速提供高级信息的用户。这些用户生成有价值的数据，如果处理不当，可能会泄露敏感信息。因此，保护​​用户隐私对于众感知系统至关重要。在本文中，我们提出了 BLIND，这是一种创新的开源真相发现系统，旨在通过在移动群智场景中使用隐私保护计算技术来提高信息质量（QoI）。BLIND 的独特之处在于它能够通过确保所涉及的各方都无法识别所提供信息的来源来保护用户隐私。该系统使用同态加密来实现著名的 K-Means 聚类算法的新颖隐私保护版本，该算法直接对加密的用户数据进行分组。然后，异常值会被私下删除，而不会向相关各方透露任何有用的信息。我们使用真实数据集和合成数据集广泛评估了所提出的系统的服务器端和客户端可扩展性以及真相发现的准确性，以在具有挑战性的条件下测试系统。与四种最先进方法的比较表明，BLIND 通过有效减轻四种不同安全攻击的影响来优化 QoI，并且比竞争对手具有更高的准确性和更低的通信开销。通过本文提出的优化，BLIND 的速度比基线系统快三倍，并且获得的均方根误差 (RMSE) 值比其他最先进的方法低 42%。