RPPTD: Robust Privacy-Preserving Truth Discovery Scheme
| Autor: | Jingxue Chen, Yong Xiang, Yining Liu, Keshav Sood |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computer Networks and Communications
Wireless network Computer science business.industry Reliability (computer networking) 020206 networking & telecommunications Usability 02 engineering and technology Trusted third party Object (computer science) Computer security computer.software_genre Encryption Computer Science Applications Information sensitivity Control and Systems Engineering Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering business computer Information Systems |
| Zdroj: | IEEE Systems Journal. 16:4525-4531 |
| ISSN: | 2373-7816 1932-8184 |
| DOI: | 10.1109/jsyst.2021.3099103 |
| Popis: | Benefiting from the rapid development of communication technology and Internet of Things (IoT) devices, crowdsensing is on the rise. Sensor data from IoT devices can be requested for data analysis and utilization, however, the collected data of an object from multiple devices are usually different. Therefore, how to extract the most reliable data from numerous data has become an important topic, and truth discovery receives great attention. These collected data often contain personal sensitive information, if users’ privacy cannot be protected, many users are unwilling to contribute their data, and the usability of the published data will be greatly reduced. In this article, a robust privacy-preserving truth discovery scheme is proposed to simultaneously achieve the reliability and privacy of data. Specifically, the data are collected and encrypted before it is sent from the user. Compared with the existing works, there are two additional benefits, trusted third party and noncolluding platforms are not necessary anymore, hence the robustness is improved and single-point failure bottlenecks are eliminated. Besides, the proposed RPPTD is secure against many known attacks in open wireless networks, and the human-factor-aware differential aggregation attack. Finally, the performance evaluation indicates that our scheme is efficient and suitable for the practical environment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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