TWC: Medium: Privacy Preserving Computation in Big Data Clouds

TWC：中：大数据云中的隐私保护计算

• 批准号: 1564097
• 负责人: Ling Liu
• 金额: $ 120万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564097&HistoricalAwards=false
• 关键词: TWC; Medium; Privacy; Preserving; Computation

Privacy is critical to freedom of creativity and innovation. Assured privacy protection offers unprecedented opportunities for industry innovation, science and engineering discovery, as well as new life enhancing experiences and opportunities. The ability to perform efficient and yet privacy preserving big data computations in the Cloud holds great potential for safe and effective data analytics, such as enabling health-care applications to provide personalized medical treatments using an individual's DNA sequence, or enabling advertisers to create targeted advertisements by mining a user's clickstream and social activities, without violation of data privacy. The PrivacyGuard project is developing algorithms, systems and tools that provide end-to-end privacy guarantees over the life cycle of a data analytic job. The end-to-end privacy guarantee can be measured by how difficult one can learn about some of the original sensitive data from the sanitized data releases, the intermediate results of execution and the output of an analytic job. The ultimate goal of PrivacyGuard is to develop a methodical framework and a suite of techniques for ensuring distributed computations to meet the desired privacy requirements of input data, as well as protecting against disclosure of sensitive patterns during execution and in the final output of the computation.The PrivacyGuard project advances the knowledge and understanding of privacy preserving distributed computation from three perspectives: (1) It designs formal mechanisms to formulate a data owner's end-to-end privacy requirement for each data release, for example, by associating each data release with a well-defined usage scope to confine the set of data analytics models and algorithms that can operate on the released data. (2) It develops a suite of execution privacy guards with dual objectives: to audit and enforce privacy compliances during distributed computation against data-flow based privacy violations and to guard the compliance of input privacy. (3) It devises a proactive approach to output privacy against information leakages associated with mining output, for example, by leveraging differential privacy model to maximize the upper bound for data privacy guarantee and minimize the lower bound for data utility losses. The PrivacyGuard project is the first effort towards a practical and systematic implementation framework for ensuring the end-to-end privacy in distributed big data computations. Furthermore, by integrating the PrivacyGuard research with the curriculum development on big data systems and analytics courses at Georgia Institute of Technology, it contributes to the education and training of new generation of data scientists to be the privacy compliance advocates.

隐私对于创造力和创新的自由至关重要。有保障的隐私保护为行业创新、科学和工程发现以及增强新生活的体验和机会提供了前所未有的机会。在云中执行高效且保护隐私的大数据计算的能力为安全有效的数据分析带来了巨大的潜力，例如使医疗保健应用程序能够使用个人的 DNA 序列提供个性化的医疗服务，或者使广告商能够创建有针对性的广告通过挖掘用户的点击流和社交活动，而不侵犯数据隐私。 PrivacyGuard 项目正在开发算法、系统和工具，在数据分析工作的生命周期中提供端到端的隐私保证。 端到端的隐私保证可以通过从清理后的数据发布、执行的中间结果和分析作业的输出中了解一些原始敏感数据的难度来衡量。 PrivacyGuard 的最终目标是开发一个系统框架和一套技术，以确保分布式计算满足输入数据所需的隐私要求，并防止在执行期间和计算的最终输出中泄露敏感模式。 PrivacyGuard项目从三个角度推进了对隐私保护分布式计算的认识和理解：（1）它设计了正式的机制来制定数据所有者对每个数据发布的端到端隐私要求，例如通过将每个数据发布与明确的使用范围限制可以对发布的数据进行操作的数据分析模型和算法集。 （2）它开发了一套具有双重目标的执行隐私卫士：在分布式计算过程中针对基于数据流的隐私违规进行审计和强制执行隐私合规性，并保护输入隐私的合规性。 （3）设计了一种主动的方法来输出隐私，防止与挖掘输出相关的信息泄漏，例如，利用差分隐私模型来最大化数据隐私保证的上限并最小化数据效用损失的下限。 PrivacyGuard项目是第一个为确保分布式大数据计算中的端到端隐私而建立实用且系统的实施框架的努力。此外，通过将 PrivacyGuard 研究与佐治亚理工学院大数据系统和分析课程的课程开发相结合，有助于教育和培训新一代数据科学家成为隐私合规倡导者。