CAREER: An Integrated Approach For Efficient Privacy Preserving Distributed Data Analytics

职业：高效隐私保护分布式数据分析的综合方法

• 批准号: 0845803
• 负责人: Murat Kantarcioglu
• 金额: $ 40万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845803&HistoricalAwards=false
• 关键词: CAREER; Integrated; Approach; Efficient; Privacy

Increasingly, different organizations need to securely share their private data to execute many critical tasks. Recently, several different approaches based on secure multi-party computation (SMC) and data sanitization techniques have emerged to enable privacy preserving distributed data analytics. Although SMC based privacy-preserving protocols allow the participating parties to learn only the final (accurate) result, they do not scale well for large amounts of data. On the other hand, sanitization based techniques allow organizations to reveal privacy sensitive data under some privacy guarantees by distorting the data. In many cases, significant data distortion that is needed to preserve privacy could lead to inaccurate results. Due to the limitations of the current approaches, efficient and accurate privacy-preserving solutions are needed for handling large distributed data sets. To address this challenge, we design and develop a novel framework where sanitization and SMC techniques are integrated to develop efficient privacy-preserving solutions under resource constraints. Basically, we use the data sanitization techniques to get initial approximate results and carry out SMC operations selectively to increase the accuracy. Since we use existing techniques in a black box fashion, our approach is orthogonal to any new sanitization or SMC techniques.Our new techniques will substantially decrease the cost of executing privacy-preserving distributed data analytics protocols. This will have a direct economic impact by opening the way for new applications (e.g., e-health and e-government applications) that are at present considered infeasible due to the lack of necessary privacy-preserving solutions that can work efficiently on large data sets.

越来越多的组织需要安全地共享其私有数据以执行许多关键任务。最近，出现了几种基于安全多方计算（SMC）和数据消毒技术的不同方法，以实现隐私保护的分布式数据分析。虽然基于SMC的隐私保护协议允许参与方仅了解最终（准确）结果，但它们不能很好地扩展大量数据。另一方面，基于净化的技术允许组织通过扭曲数据来在某些隐私保证下揭示隐私敏感数据。在许多情况下，保护隐私所需的重大数据失真可能会导致不准确的结果。由于目前方法的局限性，需要有效和准确的隐私保护解决方案来处理大型分布式数据集。为了应对这一挑战，我们设计和开发了一个新的框架，其中的消毒和SMC技术相结合，开发高效的隐私保护解决方案的资源限制下。基本上，我们使用数据净化技术来获得初始近似结果，并有选择地进行SMC操作以提高精度。由于我们以黑盒方式使用现有技术，因此我们的方法与任何新的清理或SMC技术正交。我们的新技术将大大降低执行隐私保护分布式数据分析协议的成本。这将通过为新的应用开辟道路而产生直接的经济影响（例如，电子保健和电子政务应用程序），目前由于缺乏能够有效处理大型数据集的必要的隐私保护解决方案，这些应用程序被认为是不可行的。