Secure data deduplication for Internet-of-things sensor networks based on threshold dynamic adjustment

Abstract

Large amount of data are being produce by Internet-of-things sensor networks and applications. Secure and efficient deduplication of Internet-of-things data in the cloud is vital to the prevalence of Internet-of-things applications. In order to ensure data security for deduplication, different data should be assigned with different privacy levels. We propose a deduplication scheme based on threshold dynamic adjustment to ensure the security of data uploading and related operations. The concept of the ideal threshold is introduced for the first time, which can be used to eliminate the drawbacks of the fixed threshold in traditional schemes. The item response theory is adopted to determine the sensitivity of different data and their privacy score, which ensures the applicability of data privacy score. It can solve the problem that some users care little about the privacy issue. We propose a privacy score query and response mechanism based on data encryption. On this basis, the dynamic adjustment method of the popularity threshold is designed for data uploading. Experiment results and analysis show that the proposed scheme based on threshold dynamic adjustment has decent scalability and practicability.