Biblio

As it was difficult for the State Grid Corporation of China (SGCC) to manage a large amount of safety equipment efficiently, resulting in the frequent occurrence of safety accidents caused by the quality of equipment. Therefore, this paper presents a design of a self-powered wireless communication radio frequency identification tag system based on the Si24R1. The system uses blockchain technology to provide a full-length, chain-like path for RFID big data to achieve data security management. Using low-power Si24R1 chips to make tags can extend the use time of tags and achieve full life cycle management of equipment. In addition, a transmission scheme was designed to reduce the packet loss rate, in this paper. Finally, the result showed that the device terminal received and processed information from the six tags simultaneously. According to calculations, this electronic tag could be used for up to three years. This system can be widely used for safe operation management, which can effectively reduce the investment of manpower and material resources.

Matrix multiplication computation (MMC) is a common scientific and engineering computational task. But such computation involves enormous computing resources for large matrices, which is burdensome for the resource-limited clients. Cloud computing enables computational resource-limited clients to economically outsource such problems to the cloud server. However, outsourcing matrix multiplication to the cloud brings great security concerns and challenges since the matrices and their products often usually contains sensitive information. In a previous work, Lei et al. [1] proposed an algorithm for secure outsourcing MMC by using permutation matrix and the authors argued that it can achieve data privacy. In this paper, we first review the design of Lei's scheme and find a security vulnerability in their algorithm that it reveals the number of zero element in the input matrix to cloud server. Then we present a new verifiable, efficient, and privacy preserving algorithm for outsourcing MMC, which can protect the number privacy of zero elements in original matrices. Our algorithm builds on a series of carefully-designed pseudorandom matrices and well-designed privacy-preserving matrix transformation. Security analysis shows that our algorithm is practically-secure, and offers a higher level of privacy protection than the state-of-the-art algorithm.

In the cloud storage, users lose direct control over their data. How to surely delete data in the cloud becomes a crucial problem for a secure cloud storage system. The existing way to this problem is to encrypt the data before outsourcing and destroy the encryption key when deleting. However, this solution may cause heavy computation overhead for the user-side and the encrypted data remains intact in the cloud after the deletion operation. To solve this challenge problem, we propose a novel method to surely delete data in the cloud storage by overwriting. Different from existing works, our scheme is efficient in the user-side and is able to wipe out the deleted data from the drives of the cloud servers.