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A Heuristic K-Anonymity Based Privacy Preserving for Student Management Hyperledger Fabric blockchain

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Abstract

The education sector has been experiencing the progressive popularity of the Internet of Things (IoT) systems with a range of applications that can monitor simple attendance to physical locations. IoT systems in the education sector are smoothly integrated for managing marks’ digital copies, providing certificates related to medical purposes, parental consent, and so on, due to the Hyperledger blockchain fabric’s emergence. The Hyperledger blockchain fabric’s ability to yield multiple privacy options depending on the use-case is its key benefit. Access policies govern access to the channel’s resources (ledger state, transactions, and chaincodes) are used for configuration of the Hyperledger Fabric’s channels. Due to this, the confidentiality and privacy of the singular information enclosed in the nodes of the channel are preserved. A better quality of robustness is accomplished by the channels when a node is down given an alternative to arriving at the destination whilst also given the scalability to ensure the large data amounts’ efficient sharing. Leakage of information from people with high-level access can occur when, despite data privacy, there is no accomplishment of data anonymization. It is crucial to anonymize research data because of the extensive utilization of Artificial Intelligence Markup Language (AIML) in education. Data from an individual is utilized for the generation of an anonymized dataset. Proposal of a heuristic K-anonymity privacy-preserving technique for the hyper-ledger blockchain through the addition of a privacy system manager has been given in the work. The Non-deterministic Polynomial (NP) problem is overcome by these proposed privacy-preserving techniques through the utilization of stochastic diffusion search optimization algorithm gives a better results in various parameters like throughput, latency, misclassification rate, loss metric.

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Authors and Affiliations

  1. Department of Computer Science and Engineerig, School of Computing, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Kanchipuram, Chennai, India

    B. Sowmiya & E. Poovammal

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  1. B. Sowmiya
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  2. E. Poovammal
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Correspondence to B. Sowmiya.

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Sowmiya, B., Poovammal, E. A Heuristic K-Anonymity Based Privacy Preserving for Student Management Hyperledger Fabric blockchain. Wireless Pers Commun 127, 1359–1376 (2022). https://doi.org/10.1007/s11277-021-08582-1

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