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Privacy-preserving data analysis
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作者 Yara Maha Dolla Ali 《Advances in Engineering Innovation》 2024年第2期32-36,共5页
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 re... 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. 展开更多
关键词 privacy-preserving data dnalysis differential privacy homomorphic encryption secure multi-party computation data obfuscation
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