Secure IoT Data Outsourcing With Aggregate Statistics and Fine-Grained Access Control

With the rapid development of the Internet of Things (IoT) and the dramatic increase of IoT devices, the desire to outsource huge amounts of IoT data to the cloud becomes more urgent than ever. In order to ensure the confidentiality, IoT data are usually encrypted before they are outsourced to the cloud, which will inevitably hinder the statistical analysis of them. Homomorphic encryption is an alternative to achieve the computation of encrypted data, but its inefficiency makes it not practical in the IoT environment. Another problem comes with the encryption is how to enable IoT data to be accessed by users who possess a certain set of attributes defined by data owners. In this paper, we propose a novel and practical IoT data outsourcing scheme based on Corrigan-Gibbs et al. 's computation of aggregate statistics and the ciphertext-policy attribute-based encryption (CP-ABE). It supports both secure aggregation and fine-grained access control of outsourced IoT data. Users only have to bear a small amount of computation in the process of data upload and recovery. Security analysis demonstrates that our scheme well protects the confidentiality of IoT data. A thorough and detailed performance comparison shows that our scheme enjoys a better performance on both the client side and the fog server side.