12/8/2022 0 Comments Matrix medical network![]() ![]() Data can be packaged and written to the blockchain through hash operations, and security is ensured by consensus algorithms and asymmetric cryptography of nodes within the blockchain P2P network system. Therefore, it is of practical importance to develop different privacy protection schemes according to the privacy protection needs of different attributes, respectively.Ä«lockchain is a decentralized, collectively maintained, secure, and trustworthy structure, which is ideal for storing and protecting private data to avoid large-scale data loss or leakage caused by attacks on centralized institutions. Some attributes need to be kept secret strictly, so they should be confused carefully even at the expense of certain usability, while some attributes do not need strict confidentiality, so they can be handled based on the principle of ensuring usability and reducing the deviation rates as much as possible. In some cases, the dataset may contain the attributes of different privacy levels. And then, analysis of these published data may lead to wrong or deviation results. If the traditional method such as direct Laplace noise is adopted, it will add too much noise to the massive data, which will lead to a great distortion of the data. ![]() However, data from medical care, finance, and public security are often of high dimensionality and have a large number of records. Traditionally, the sensitive data can be protected by the classical Laplace algorithm to add noise perturbation directly to the data, which makes it difficult to know the exact single data records while ensuring the relative approximation of statistical results. In order to avoid the leakage of sensitive data, it is wise to take some measures to protect sensitive data. The leakage of such sensitive information may lead to unpredictable consequences such as kidnapping or blackmail and so on. However, the published data may contain extremely sensitive data, which can be collected and sold by third parties. Many organizations related to medical, finance, public security, and other fields often need to outsource their data to third parties for analysis and to help them make decisions. With the arrival of the era of big data and cloud computing, the data center of each city is full of all kinds of high-dimensional data. Publishing and sharing private data on this model not only resist strong background knowledge attacks from adversaries outside the system but also prevent stealing and tampering of data by not-completely-honest participants inside the system. Further, we combine this algorithm with blockchain and propose an Efficient Privacy Data Publishing and Sharing Model based on the blockchain. Our experimental results show that the mean square error of the proposed algorithm is smaller than the traditional differential privacy algorithm with the same privacy parameters, and the computational efficiency can be improved. Meanwhile, the use of the divided-block scheme and the sparse matrix transformation scheme can improve the computational efficiency of the principal component analysis method for handling large amounts of high-dimensional sensitive data, and we demonstrate that the proposed algorithm satisfies differential privacy. In this algorithm, different levels of privacy budget parameters are assigned to different attributes according to the required privacy protection level of each attribute, taking into account the privacy protection needs of different levels of attributes. To address the above problem, we design a Divided-block Sparse Matrix Transformation Differential Privacy Data Publishing Algorithm (DSMT-DP). However, these algorithms are inefficient in processing and do not take into account the different privacy protection needs of each attribute in high-dimensional datasets. Many researchers seek to realize differential privacy protection in massive high-dimensional datasets using the method of principal component analysis. ![]() At the same time, it becomes a great concern that how to obtain optimal outputs from big data publishing and sharing management while protecting privacy.
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