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摘要
Recently, the Internet of Things (IoT) has penetrated many aspects of the physical world to realize different applications. Through IoT, these applications generate, exchange, aggregate, and analyze a vast amount of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. Therefore, it is rather necessary for IoT systems to be equipped with the ability to resist security and privacy risks when fulfilling the desired functional requirements and services. To achieve these goals, there are many new challenges for IoT to implement privacy preserving data manipulation. First, data analysts need to process privacy-sensitive data to extract the expected information without privacy disclosure. In addition, many privacy related factors, including privacy valuation and risk assessment, affect sensitive and private data trading between data owners and requesters. Moreover, the data owners' security behavior also plays an important role in privacy protection in IoT applications. Concerning these issues, this article introduces and surveys privacy preserving techniques in the processes of data aggregation, trading, and analysis: the balance between data analysis and privacy preservation from the data analysts' perspective, secure data trading from the perspective of data owners and requesters, and secure private data aggregation from the data owners' perspective.
