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隐私计算——概念、计算框架及其未来发展趋势
Fenghua Li, Hui Li, Ben Niu, Jinjun Chen
工程（英文） ›› 2019, Vol. 5 ›› Issue (6) : 1179-1192.
PDF(1409 KB)
PDF(1409 KB)
隐私计算——概念、计算框架及其未来发展趋势
Privacy Computing: Concept, Computing Framework, and Future Development Trends
随着信息技术的快速发展和个性化服务的不断演进，大型互联网公司在服务用户过程中积累了海数据。此外，数据的频繁跨境、跨系统、跨生态圈交互已成为常态，加剧了隐私信息在不同信息系统中有意/无意留存，但随之而来的隐私信息保护短板效应、隐私侵犯追踪溯源难等问题越来越严重，致使现有的隐私保护方案不能提供体系化的保护。本文从信息采集、存储、处理、发布（含交换）、销毁等全生命周期的各个环节角度出发，阐明了现有常见应用场景下隐私保护算法的局限性，提出了隐私计算理论及关键技术体系，其核心内容包括：隐私计算框架、隐私计算形式化定义、隐私计算应遵循的四个原则、算法设计准则、隐私保护效果评估、隐私计算语言等内容。最后以四个应用场景为示例描述了隐私计算的普适性应用，并展望了隐私计算的未来研究方向和待解决问题，期待指引开放环境下用户隐私保护等方面的理论与技术研究。
With the rapid development of information technology and the continuous evolution of personalized services, huge amounts of data are accumulated by large Internet companies in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional persistence of private information in different information systems. However, problems such as the cask principle of preserving private information among different information systems and the difficulty of tracing the source of privacy violations are becoming increasingly serious. Therefore, existing privacy-preserving schemes cannot provide systematic privacy preservation. In this paper, we examine the links of the information life-cycle, such as information collection, storage, processing, distribution, and destruction. We then propose a theory of privacy computing and a key technology system that includes a privacy computing framework, a formal definition of privacy computing, four principles that should be followed in privacy computing, algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing language. Finally, we employ four application scenarios to describe the universal application of privacy computing, and discuss the prospect of future research trends. This work is expected to guide theoretical research on user privacy preservation within open environments.
隐私计算 / 隐私信息描述 / 隐私度量 / 隐私保护效果评估 / 隐私计算语言
Privacy computing / Private information description / Privacy metric / Evaluation of the privacy-preserving effect / Privacy computing language
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