With the development of the Industrial Internet of Things (IIoT), the scale of IIoT data is rapidly increasing, bringing significant challenges to existing data management systems. To tackle this issue, we propose a similarity-based secure deduplication for IIoT cloud management system, which can effectively balance the security and availability of IIoT data and minimize the storage cost. Concretely, we propose a similarity-based secure deduplication algorithm for IIoT (IIoT-SBSD) by designing a similarity-preserving tag (IIoT-Simhash). This algorithm can perform similarity deduplication over ciphertexts, thus reducing storage space while ensuring data security. Besides, we construct a parallelizable edge-based deduplication framework in which similarity comparison and deduplication operations are performed directly by edge nodes, significantly alleviating the transmission pressure. Additionally, we propose similarity-based proofs of ownership, S-PoWs, to mitigate the impact of data deduplication on the user's access to the IIoT data. Experimental results show that our method significantly reduces storage space and transmission bandwidth without increasing the computation burden.

Local differential privacy (LDP) collects user data while protecting user privacy and eliminating the need for a trusted data collector. Several LDP protocols have been proposed and deployed in real-world applications. Frequency estimation is a fundamental task in the LDP protocols, which enables more advanced tasks in data analytics. However, the existing LDP protocols amplify the added noise in estimating the frequencies and therefore do not achieve optimal performance in accuracy. This paper introduces a convolution framework to analyze and optimize the estimated frequencies of LDP protocols. The convolution framework can equivalently transform the original frequency estimation problem into a deconvolution problem with noise. We thus add the Wiener filter-based deconvolution algorithms to LDP protocols to estimate the frequency while suppressing the added noise. Experimental results on different real-world datasets demonstrate that our proposed algorithms can lead to significantly better accuracy for state-of-the-art LDP protocols by orders of magnitude for the smooth dataset. And these algorithms also work on non-smooth datasets, but only to a limited extent. Our code is available at https://github.com/SEUNICK/LDP.