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.

隐私对创造力和创新自由至关重要。保证的隐私保护为行业创新，科学和工程发现提供了前所未有的机会，以及新的增强生活的体验和机会。在云中执行高效但因此保留大数据计算的能力具有很大的潜力，可以进行安全有效的数据分析，例如使医疗保健应用程序使用个人的DNA序列提供个性化的医疗治疗，或者使广告客户能够通过挖掘用户的点击活动和社交活动，而无需侵犯数据隐私。 PrivacyGuard项目正在开发在数据分析工作的生命周期中提供端到端隐私保证的算法，系统和工具。 端到端隐私保证可以通过从消毒数据发布，执行的中间结果和分析作业的中间结果中了解一些原始敏感数据的困难来衡量。隐私组织的最终目标是制定有条不紊的框架和一套技术，以确保分布式计算满足输入数据的所需隐私要求，并保护在执行过程中和计算的最终输出期间披露敏感模式的披露，并在计算的最终输出中。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定义明确的使用范围关联，以限制可以在已发布数据上操作的数据分析模型和算法集。 （2）它开发了具有双重目标的一套执行隐私警卫：在分布式计算期间针对基于数据流的隐私侵犯的分布式计算时进行审核和执行隐私权，并保留输入隐私的合规性。 （3）它为与采矿输出相关的信息泄漏设计了一种主动的输出隐私方法，例如，通过利用差异隐私模型来最大化数据隐私保证的上限，并最大程度地减少数据实用程序损失的下限。 PrivacyGuard项目是为确保分布式大数据计算中端到端隐私的实用和系统实施框架的首次努力。此外，通过将佐治亚理工学院大数据系统和分析课程的课程开发与课程开发相结合，它有助于对新一代数据科学家的教育和培训成为隐私合规性的提倡者。