敏感和私人数据分析:系统回顾

Syeda Sana Zainab, Mohand Tahar Kechadi
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引用次数: 3

摘要

每天,电子商务、IT、医院、零售和供应链等各种组织都会产生大量数据。由于计算机设备的扩展和技术的进步,大量的数据被收集和分析以支持决策。对这些数据的研究正在推动商业发展,并在许多不同领域为社会做出有益的贡献。然而,由于潜在敏感数据的存储和流动,引发了严重的隐私问题[31]。允许从数据中提取知识,同时保护隐私的策略称为保护隐私的数据挖掘(PPDM)技术。本文综述了利用各种PPDM算法和技术对私有和敏感数据的分析。我们还强调了它们在各种情况下的优势和局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensitive and Private Data Analysis: A Systematic Review
Each day an extensive amount of data is produced from various organisations, such as e-commerce, IT, hospitals, retail and supply chain, etc. Due to the expansion of computer devices and advances in technology this immense amount of data has been collected and analysed to support decision making. The examination of such data is advancing businesses and contributing advantageously to society in numerous diverse areas. However, serious privacy concerns are raised due to the storage and flow of potentially sensitive data [31]. Strategies that permit the knowledge extraction from the data, while protecting privacy, are known as privacy-preserving data mining (PPDM) techniques. This paper surveys the analysis of private and sensitive data using various PPDM algorithms and techniques. We also highlighted their advantages and limitations within various contexts.
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