Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance

Obinna Donald, Olakunle Abayomi Ajala, Chuka Anthony Arinze, Onyeka Chrisanctus Ofodile, Chinwe Chinazo Okoye, Obinna Donald Daraojimba
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Abstract

In the contemporary landscape of big data analytics, privacy concerns loom large, exacerbated by escalating surveillance measures. This review delves into the advancements of privacy-enhancing technologies (PETs) amidst this era of heightened scrutiny. The review explores the evolving landscape of PETs, highlighting their pivotal role in safeguarding individual privacy while enabling meaningful data analysis. Firstly, the review elucidates the escalating surveillance environment, characterized by ubiquitous data collection practices and the proliferation of sophisticated monitoring mechanisms. Against this backdrop, the imperative for robust privacy solutions becomes evident. Subsequently, the review navigates through the array of PETs, encompassing differential privacy, homomorphic encryption, secure multi-party computation, and federated learning, among others. Each technology is scrutinized for its efficacy in mitigating privacy risks without compromising analytical utility. Furthermore, the review delineates notable applications of PETs across diverse domains, including healthcare, finance, and social media. Case studies exemplify how PETs facilitate data sharing and collaborative analysis while preserving confidentiality and compliance with regulatory frameworks. Moreover, the review examines the challenges hindering the widespread adoption of PETs, such as computational overhead, interoperability issues, and regulatory ambiguities. Strategies for overcoming these hurdles are elucidated, encompassing advancements in algorithmic efficiency, standardization efforts, and policy advocacy. The review underscores the pivotal role of PETs in reconciling the imperatives of data analytics with the imperatives of privacy protection amidst escalating surveillance. It emphasizes the necessity for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to foster the development and deployment of effective PET solutions, thereby ensuring a harmonious balance between data utility and individual privacy rights in the digital age.
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在监控日益加强的时代,审查用于海量数据分析的隐私增强技术的进展情况
在大数据分析的当代环境中,隐私问题迫在眉睫,而不断升级的监控措施又加剧了这一问题。本综述深入探讨了隐私增强技术(PET)在这一高度审查时代的发展。综述探讨了隐私增强技术不断发展的情况,强调了这些技术在保障个人隐私和进行有意义的数据分析方面的关键作用。首先,综述阐明了不断升级的监控环境,其特点是无处不在的数据收集做法和复杂监控机制的扩散。在此背景下,强大的隐私解决方案的必要性变得显而易见。随后,评述浏览了一系列 PET,包括差分隐私、同态加密、安全多方计算和联合学习等。每种技术在降低隐私风险的同时又不影响分析的实用性,对其功效进行了仔细研究。此外,综述还描述了 PET 在医疗保健、金融和社交媒体等不同领域的显著应用。案例研究举例说明了 PET 如何促进数据共享和协作分析,同时维护保密性并遵守监管框架。此外,综述还探讨了阻碍 PETs 广泛应用的挑战,如计算开销、互操作性问题和监管模糊性。综述阐明了克服这些障碍的策略,包括算法效率的提高、标准化工作和政策宣传。综述强调,在监控不断升级的情况下,PET 在协调数据分析的必要性和隐私保护的必要性方面发挥着关键作用。它强调了研究人员、政策制定者和行业利益相关者之间开展跨学科合作的必要性,以促进开发和部署有效的 PET 解决方案,从而确保在数字时代实现数据实用性与个人隐私权之间的和谐平衡。
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