Privacy-Preserving data analysis
- 1 Capitol Technology University
* Author to whom correspondence should be addressed.
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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Cite this article
Ali,Y.M.D. (2024). Privacy-Preserving data analysis. Advances in Engineering Innovation,7,32-36.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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