A lightweight and efficient raw data collection scheme for IoT systems - Yixuan Huang; Yining Liu; Jingcheng Song; Weizhi Meng (Journal of Information and Intelligence)
With the prevalence of Internet of Things (IoT) devices, data collection has the potential to improve people's lives and create a significant value. However, it also exposes sensitive information, which leads to privacy risks. An approach called N-source anonymity has been used for privacy preservation in raw data collection, but most of the existing schemes do not have a balanced efficiency and robustness. In this work, a lightweight and efficient raw data collection scheme is proposed. The proposed scheme can not only collect data from the original users but also protect their privacy. Besides, the proposed scheme can resist user poisoning attacks, and the use of the reward method can motivate users to actively provide data. Analysis and simulation indicate that the proposed scheme is safe against poison attacks. Additionally, the proposed scheme has better performance in terms of computation and communication overhead compared to existing methods. High efficiency and appropriate incentive mechanisms indicate that the scheme is practical for IoT systems.