Abstract
The privacy and utility are treated as a major factor in influencing the role of data privacy preservation in cloud environments. There exists a trade-off between these two factors, where one factor should compromise its functionality over the other. It is hence necessary to maintain both utility and privacy for a data offloaded or accessed across cloud computing environment. In this paper, we develop a utility privacy model that established utility using Deep Adaptive Clustering (DAC) and privacy using Elliptic Curve Digital Signature Algorithm (ECDSA). The utility is performed using clustering of the input datasets using DAC and the privacy is maintained using ECDSA. The simulation is conducted on specific datasets to test the efficacy of the model and the results shows improved accuracy on clustering, and efficient privacy metrics than existing methods.
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The data that support the findings of this study are available from the corresponding author, upon reasonable request.
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Conceptualization: NY; Methodology: KP; Formal analysis and investigation: TK; Writing—original draft preparation: KP, TK; Writing—review and editing: KP, TK.
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Yuvaraj, N., Praghash, K. & Karthikeyan, T. Data Privacy Preservation and Trade-off Balance Between Privacy and Utility Using Deep Adaptive Clustering and Elliptic Curve Digital Signature Algorithm. Wireless Pers Commun 124, 655–670 (2022). https://doi.org/10.1007/s11277-021-09376-1
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DOI: https://doi.org/10.1007/s11277-021-09376-1