EAPSI: Creating Fast and Accurate Data Mining Algorithms that Preserve Privacy

EAPSI：创建快速、准确的数据挖掘算法来保护隐私

• 批准号: 1613892
• 负责人: Benjamin Fish
• 金额: $ 0.54万
• 依托单位: Fish Benjamin S
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-15 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1613892&HistoricalAwards=false
• 关键词: EAPSI; Creating; Fast; Accurate; Data

This project will use new techniques to create fast and accurate algorithms that preserves the privacy of people's data in big data mining, i.e. tasks that require large amounts of data. While big data mining has extraordinarily broad and important uses, releasing the output of data mining algorithms to data analysts and the public can result in leaking the private data of individuals whose data is being used. Even when data mining techniques are able to keep data private, they may be too slow or inaccurate to be used widely in practice. Previous approaches to big data mining tasks have typically either focused on a) keeping private data private or b) achieving fast and accurate algorithms, but not both. This research aims to find algorithms for big data mining tasks that are simultaneously privacy-preserving, fast, and accurate, to the degree that this is possible. This project will be conducted at The University of Melbourne with Benjamin Rubinstein, a leading expert in fast techniques for achieving privacy-preserving algorithms.Focus will be on finding such fast differentially-private techniques for the Frequent Itemset mining problem (FI), a fundamental question in knowledge discovery about extracting frequently occurring sets of items in a database, and related problems. Recent approaches have demonstrated fast and accurate approximation algorithms for these types of problems. Previous methods for solving this problem were not necessarily amenable for achieving privacy-preservation quickly and accurately. These new approaches, however, which involve sampling techniques and using under-utilized tools from the theory of machine learning, are especially helpful for creating privacy-preserving algorithms. This research will leverage this approach to create algorithms that are not only fast and accurate, but also keep individuals' data private. This research will also analyze the tradeoffs between speed, accuracy, and privacy in such solutions.This award under the East Asia and Pacific Summer Institutes program supports summer research by a U.S. graduate student and is jointly funded by NSF and the Australian Academy of Science.

该项目将使用新技术来创建快速准确的算法，以在大数据挖掘中保留人们数据的隐私，即需要大量数据的任务。 尽管大数据挖掘具有非常广泛而重要的用途，但向数据分析师释放了数据挖掘算法的输出，公众可以泄露使用数据的个人的私人数据。即使数据挖掘技术能够将数据私有化，它们也可能太慢或不准确，无法在实践中广泛使用。大数据挖掘任务的先前方法通常集中在a）私有数据或b）实现快速，准确的算法，但并非两者兼而有之。这项研究旨在找到用于同时拥有隐私，快速和准确的大数据挖掘任务的算法，以至于这是可能的。该项目将在墨尔本大学与本杰明·鲁宾斯坦（Benjamin Rubinstein）一起进行，本杰明·鲁宾斯坦（Benjamin Rubinstein）是实现隐私保护算法的快速技术专家。Focus将在为频繁的项目集挖掘问题（FI）（FI）找到如此快速的差异性技术（FI）（FI）（FI），在知识中的基本问题，这些问题经常提取问题，并且是在发现的问题中，并且是在发现的问题中，并且是在发现的问题中，并且是在事物中提取的问题。最近的方法证明了这些类型的问题的快速准确近似算法。以前解决此问题的方法不一定可以快速准确地实现隐私保护。但是，这些新方法涉及采样技术并使用机器学习理论中利用的工具不足的工具，特别有助于创建隐私的算法。这项研究将利用这种方法来创建不仅快速准确的算法，而且还可以使个人的数据私密。这项研究还将分析此类解决方案中速度，准确性和隐私之间的权衡。该奖项颁发的奖项是东亚和太平洋夏季研究所计划的奖项，支持美国研究生的夏季研究，并由NSF和澳大利亚科学学院共同资助。