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.

Resumption of virtual machines after adaptive deduplication of virtual machine images in live migration

“…High deduplication efficiency, high read throughput and high backup throughput achieved by doing the experiments on the real world VM image snapshots of the users. In [29], a secure and efficient data deduplication technique for storing the data received from IoT sensors in the cloud. SecDedoop [30] in which a secure deduplication mechanism used for access control of bigdata over Hadoop Distributed File System.…”

‏An Enhanced Approach to Improve the Security and Performance for Deduplication

“…At the present, the strategy of deletion can primarily be used at the file, block, and byte levels, and they can be optimized for storage space. As shown in Figure 3, the de-duplication levels can be divided into three levels: based on file, block and byte [23] [24].…”

Computing Time Series Data During Index Based De-Duplication of Industrial IoT Data in Cloud Environment

“…It provisions both file level and block level data de-duplication This scheme achieves consistency and provides mutual agreement with dynamic ownership management Gao et. al [19] De-duplication scheme based on threshold dynamic adjustment This scheme determines the sensitivity of various data with privacy score during uploading of data into the storage system This scheme supports de-duplication with lower privacy and not for higher extent. Sharma et.…”

“…The main task to be performed in de-duplication is partitioning of data [11], [18], [22]. As deduplication is carried out with protective data various security measures are considered [10], [12][13][14][15], [19]. While decrypting the data for the deduplication process many attackers may intrude to steal the data which is addressed with various potentials in [16], [17].…”