Privacy preserving big data analytics: A critical analysis of state‐of‐the‐art,WIREs Data Mining and Knowledge Discovery

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Privacy preserving big data analytics: A critical analysis of state‐of‐the‐art
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2020-10-07 , DOI: 10.1002/widm.1387
M. Ileas Pramanik ₁ , Raymond Y. K. Lau ₂ , Md Sakir Hossain ₃ , Md Mizanur Rahoman ₁ , Sumon Kumar Debnath ₄ , Md Golam Rashed ₅ , Md Zasim Uddin ₁

Affiliation

In the era of “big data,” a huge number of people, devices, and sensors are connected via digital networks and the cross‐plays among these entities generate enormous valuable data that facilitate organizations to innovate and grow. However, the data deluge also raises serious privacy concerns which may cause a regulatory backlash and hinder further organizational innovation. To address the challenge of information privacy, researchers have explored privacy‐preserving methodologies in the past two decades. However, a thorough study of privacy preserving big data analytics is missing in existing literature. The main contributions of this article include a systematic evaluation of various privacy preservation approaches and a critical analysis of the state‐of‐the‐art privacy preserving big data analytics methodologies. More specifically, we propose a four‐dimensional framework for analyzing and designing the next generation of privacy preserving big data analytics approaches. Besides, we contribute to pinpoint the potential opportunities and challenges of applying privacy preserving big data analytics to business settings. We provide five recommendations of effectively applying privacy‐preserving big data analytics to businesses. To the best of our knowledge, this is the first systematic study about state‐of‐the‐art in privacy‐preserving big data analytics. The managerial implication of our study is that organizations can apply the results of our critical analysis to strengthen their strategic deployment of big data analytics in business settings, and hence to better leverage big data for sustainable organizational innovation and growth.

中文翻译：

隐私保护大数据分析：对最新技术的关键分析

在“大数据”时代，大量的人员，设备和传感器通过数字网络连接在一起，这些实体之间的相互影响产生了巨大的宝贵数据，可促进组织的创新和发展。但是，数据泛滥也引发了严重的隐私问题，这可能引起监管反弹并阻碍进一步的组织创新。为了应对信息隐私的挑战，研究人员在过去的二十年中探索了隐私保护方法。但是，现有文献中缺少对保护隐私的大数据分析的透彻研究。本文的主要贡献包括对各种隐私保护方法的系统评估，以及对最新的隐私保护大数据分析方法的严格分析。进一步来说，我们提出了一个用于分析和设计下一代隐私保护大数据分析方法的多维框架。此外，我们致力于查明将隐私保护大数据分析应用于业务环境的潜在机遇和挑战。我们提供了五项建议，以有效地将保留隐私的大数据分析应用于企业。据我们所知，这是关于隐私保护大数据分析的最新技术的第一个系统研究。我们研究的管理意义在于，组织可以应用我们的关键分析结果来加强其在业务环境中大数据分析的战略部署，从而更好地利用大数据促进组织的可持续创新和增长。

更新日期：2020-10-07

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