Privacy-preserving raw data collection without a trusted authority for IoT.

With the rapid developments of IoT technologies, a large amount of real-time data is collected and shared, having important impacts on many applications, such as business advertisement and decision-making assistance. However, most users are unwilling to share their personal data directly to any third party for either academic research or commercial analysis because personal data contains private or sensitive information, such as economic status or living habits. Balancing the utility of big data and users’ privacy is a vital issue in academia and industry. In this paper, a privacy-preserving raw data collection scheme for IoT is proposed, in which the participant's data is collected and obfuscated with the other participants’ data within a group in order to mask the individual's privacy. Specifically, individual data is kept in its raw format to enhance its value for the data consumer, while no other users outside of the user herself know the source of the collected data. In addition, no trusted authority (TA) is needed in our proposed scheme, which is more suitable for real-world applications. Moreover, efficiency analysis is performed by simulation, and the result shows that our proposed scheme is practical for IoT systems.