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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

Privacy-preserving data analysis

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David Shamoo Excel *

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
DOI url: https://doi.org/10.30574/wjarr.2024.23.3.2724
 
Received on 26 July 2024; revised on 02 September 2024; accepted on 04 September 2024
 
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.
 
Privacy-Preserving Data Analysis; Differential Privacy; Homomorphic Encryption; Secure Multi-Party Computation; Data Obfuscation.
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2024-2724.pdf

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David Shamoo Excel. Privacy-preserving data analysis. World Journal of Advanced Research and Reviews, 2024, 23(3), 597-609. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2724

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