PPAT: An effective scheme ensuring privacy-preserving, accuracy, and trust for worker selection in mobile crowdsensing networks

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-09-27 DOI:10.1016/j.future.2024.107536
Qianxue Guo , Yasha He , Qian Li , Anfeng Liu , Neal N. Xiong , Qian He , Qiang Yang , Shaobo Zhang
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Abstract

The data content privacy protection and data accuracy are two important research issues in Mobile Crowdsensing (MCS). However, current researches have rarely been able to satisfy privacy protection as well as data accuracy at the same time, thus hindering the development of MCS. To solve the above issues, for the first time, we have proposed a Privacy Preserving, Accuracy, and Trust data collection scheme (PPAT) for MCS, which can protect the privacy of data content and maintain high accuracy at low-cost style. In PPAT scheme, First, we proposed a scrambled data privacy protection framework which can protect the data of each worker from being known to any third party, which can protect the data privacy of workers. The second, more importantly, we propose a truth value estimation method based on trust computing, which can obtain the truth value more accurately compared to the classic methods under privacy-preserving. In the proposed trust-based truth value calculation, the worker's trust is determined by comparing it with the weight of the trusted worker. Then, the truth value is calculated by the trust of the workers, so that the truth value obtained is more accurate. Through theoretical analysis, it is proved that the proposed PPAT scheme has good worker data content, worker trust, and truth value content privacy protection. Through a large number of simulation experiments, the strategy proposed in this paper has a good ability to protect data content privacy compared to the previous strategy, while improving data quality by 0.5%∼5.7%, and reducing data collection costs by 35.6%∼54.9%.
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PPAT:一种有效的方案,可确保移动群感网络中工人选择的隐私保护、准确性和信任度
数据内容隐私保护和数据准确性是移动人群感应(MCS)的两个重要研究课题。然而,目前的研究很少能同时满足隐私保护和数据准确性的要求,从而阻碍了 MCS 的发展。为了解决上述问题,我们首次提出了一种适用于 MCS 的 "隐私保护、准确性和信任 "数据收集方案(PPAT),它能以低成本的方式保护数据内容的隐私并保持高准确性。在 PPAT 方案中,首先,我们提出了一个加扰数据隐私保护框架,可以保护每个工人的数据不被任何第三方知晓,从而保护工人的数据隐私。其次,更重要的是,我们提出了一种基于信任计算的真值估算方法,与隐私保护下的经典方法相比,它能更准确地得到真值。在所提出的基于信任的真相值计算方法中,工人的信任度是通过与受信任工人的权重进行比较来确定的。然后,根据工人的信任度计算出真相值,这样得到的真相值就更加准确了。通过理论分析,证明了所提出的 PPAT 方案具有良好的工人数据内容、工人信任和真值内容隐私保护能力。通过大量仿真实验,本文提出的策略与之前的策略相比,具有良好的数据内容隐私保护能力,同时数据质量提高了 0.5%∼5.7%,数据采集成本降低了 35.6%∼54.9%。
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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