Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources can leak such information. To preserve user privacy, researchers have devised different privacy-preserving approaches; however, the usability of these methods, in terms of practical use, needs careful analysis due to the high diversity and complexity of the methods. This paper presents a framework named PPaaS (Privacy Preservation as a Service) to maintain usability by employing selective privacy preservation. PPaaS includes a pool of privacy preservation methods, and for each application, it selects the most suitable one after rigorous evaluation. It enhances the usability of privacy-preserving methods within its pool; it is a generic platform that can be used to sanitize big data in a granular, application-specific manner by employing a suitable combination of diverse privacy-preserving algorithms to provide a proper balance between privacy and utility.

中文翻译：

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PPaaS：隐私保护即服务

个人身份信息 (PII) 可以通过各种渠道进入网络空间，许多潜在来源都可以泄漏此类信息。为了保护用户隐私，研究人员设计了不同的隐私保护方法；然而，由于这些方法的高度多样性和复杂性，这些方法在实际使用方面的可用性需要仔细分析。本文提出了一个名为 PPaaS（隐私保护即服务）的框架，通过采用选择性隐私保护来保持可用性。PPaaS 包含一组隐私保护方法，针对每个应用，经过严格评估，选择最合适的方法。它增强了其池中隐私保护方法的可用性；它是一个通用平台，可用于细粒度地清理大数据，