TWC: Small: Privacy Preserving Outlier Detection and Recognition

TWC：小型：隐私保护异常值检测和识别

• 批准号: 1422501
• 负责人: Jaideep Vaidya
• 金额: $ 50.85万
• 依托单位: Rutgers University Newark
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422501&HistoricalAwards=false
• 关键词: TWC; Small; Privacy; Preserving; Outlier

Big data analytics can revolutionize innovation and productivity across diverse domains. However, this requires sharing or joint analysis of data, which is often inhibited due to privacy and security concerns. While techniques have been developed to enable the safe use of data for analysis, none of these work for the critical task of outlier detection. Outlier detection is one of the most fundamental data analysis tasks, useful in applications as far ranging as homeland security, to medical informatics, to financial fraud. However, when the data is fragmented and cannot be collected together, it is impossible even to appropriately identify outliers, much less explain them. This project aims to fill this gap, and enable the secure identification and explanation of outliers without breaching the privacy of the data owners, the data custodians, or the data subjects. The potential to advance science through the discovery and analysis of exceptions can have unparalleled impact and significantly help in widening co-operation, thus preventing loss through data isolation.The project develops strong definitions for private outlier detection encompassing both process privacy and result privacy. A suite of privacy-preserving tools and techniques are then developed to enable outlier detection across different data ownership models, over a variety of multi-modal datasets, while supporting differing tradeoffs of privacy, efficiency, and utility. The research improves our scientific understanding of secure computation, data outsourcing and distributed data analysis. The project also cultivates the integration of research and education, by providing opportunities for research by undergraduates at an early stage.

大数据分析可以彻底改变不同领域的创新和生产力。然而，这需要共享或联合分析数据，由于隐私和安全问题，这通常被禁止。虽然已经开发了一些技术来安全地使用数据进行分析，但这些技术都不适用于异常值检测的关键任务。异常值检测是最基本的数据分析任务之一，在国土安全、医学信息学、金融欺诈等应用中非常有用。然而，当数据是分散的，无法收集在一起时，甚至不可能适当地识别异常值，更不用说解释它们了。该项目旨在填补这一空白，并在不侵犯数据所有者、数据保管人或数据主体隐私的情况下，安全地识别和解释离群值。通过发现和分析异常来推进科学的潜力可以产生无与伦比的影响，并极大地有助于扩大合作，从而防止数据隔离造成的损失。该项目为私有离群值检测制定了强有力的定义，包括过程隐私和结果隐私。然后，开发了一套隐私保护工具和技术，以在各种多模态数据集上实现不同数据所有权模型的离群值检测，同时支持隐私，效率和效用的不同权衡。该研究提高了我们对安全计算、数据外包和分布式数据分析的科学认识。该项目还通过在早期阶段为本科生提供研究机会，促进研究与教育的融合。