Privacy-preserving data analysis
University of Florida, United States of America.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 597–609
Article DOI: 10.30574/wjarr.2024.23.3.2724
Publication history:
Received on 26 July 2024; revised on 02 September 2024; accepted on 04 September 2024
Abstract:
With the ever-increasing volume of data being generated and shared across various platforms, the challenge of maintaining privacy while extracting value from this data has become paramount. This paper delves into the realm of Privacy-Preserving Data Analysis (PPDA), examining its current landscape and the pivotal techniques shaping it. Using datasets from diverse domains, we evaluated four leading PPDA techniques—Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computation (SMPC), and Data Obfuscation—to discern their efficacy and trade-offs in terms of data utility and privacy breach risk. Our findings underscore the strengths and constraints of each method, guiding researchers and practitioners in choosing the optimal approach for specific scenarios. As data continues to be an invaluable asset in the digital age, the tools and techniques to analyze it privately will play a critical role in shaping future data-driven decision-making processes.
Keywords:
Privacy-Preserving Data Analysis; Differential Privacy; Homomorphic Encryption; Secure Multi-Party Computation; Data Obfuscation.
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0