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Energy cost and accuracy impact of k-anonymity2022 International Conference on ICT for Sustainability (ICT4S)10.1109/ICT4S55073.2022.00018
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Energy Cost and Machine Learning Accuracy Impact of k-Anonymisation and Synthetic Data Techniques2023 International Conference on ICT for Sustainability (ICT4S)10.1109/ICT4S58814.2023.00015
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HCS: A Hybrid Data Security Enhancing Model Based on Cryptography AlgorithmsAdvances in Information Communication Technology and Computing10.1007/978-981-19-9888-1_39

A Technique for Securing Big Data Using K-Anonymization With a Hybrid Optimization Algorithm

Suman Madan, Puneet Goswami

Source Title: International Journal of Operations Research and Information Systems (IJORIS)12(4)

ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781799861232|DOI: 10.4018/IJORIS.20211001.oa3

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MLA

Madan, Suman, and Puneet Goswami. "A Technique for Securing Big Data Using K-Anonymization With a Hybrid Optimization Algorithm." IJORIS vol.12, no.4 2021: pp.1-21. http://doi.org/10.4018/IJORIS.20211001.oa3

APA

Madan, S. & Goswami, P. (2021). A Technique for Securing Big Data Using K-Anonymization With a Hybrid Optimization Algorithm. International Journal of Operations Research and Information Systems (IJORIS), 12(4), 1-21. http://doi.org/10.4018/IJORIS.20211001.oa3

Chicago

Madan, Suman, and Puneet Goswami. "A Technique for Securing Big Data Using K-Anonymization With a Hybrid Optimization Algorithm," International Journal of Operations Research and Information Systems (IJORIS) 12, no.4: 1-21. http://doi.org/10.4018/IJORIS.20211001.oa3

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Abstract

The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.