CAREER: A Theoretical Foundation for Achievability and Optimization in Privacy-Preserving Data Mining

职业：隐私保护数据挖掘可实现性和优化的理论基础数据挖掘已经成功地应用于支持各种应用，包括市场营销、天气预报、医疗诊断和国土安全。然而，在不侵犯被挖掘数据隐私的情况下挖掘数据仍然是一个关键的挑战。例如，如何挖掘patientsÕ个人信息是医疗保健应用程序中一个持续存在的问题。《健康保险流通与责任法案》（HIPAA）等新出现的隐私立法，以及公众对隐私保护的高度关注，要求计算界立即坚决关注数据挖掘中对私人信息的保护。本研究涉及对隐私保护数据挖掘中隐私保护、数据挖掘准确性和系统资源之间权衡的理解、分析和优化。该方法旨在为定义数据挖掘中隐私保护的要求、识别隐私保护策略的领域以及确定隐私保护策略的可实现性奠定坚实的理论基础。这一理论基础使设计和优化保护隐私的数据挖掘算法成为现实、通用和高效的。本项目的研究成果对nationÕs高等教育体系和高新技术产业具有更广泛的影响。在不侵犯数据所有者隐私的情况下挖掘私人数据的能力对于各种各样的公司、大学、医院和政府机构来说都是必须的。同样，在数据挖掘中保护隐私的理论和经验验证方法将使所有关心隐私的个人受益。该项目的影响还通过教育努力扩展到学术界，包括研究生和本科生培训、课程开发、研讨会和外展。

CAREER: A Theoretical Foundation for Achievability and Optimization in Privacy-Preserving Data MiningData mining has been successfully applied to support a variety of applications, including marketing, weather forecasting, medical diagnosis, and homeland security. Mining data without violating the privacy of data being mined, however, is still a critical challenge. How to mine patientsÕ personal information, for example, is an ongoing problem in healthcare applications. Emerging privacy legislation, such as the Health Insurance Portability and Accountability Act (HIPAA), as well as the heightened public concerns about privacy protection, require immediate and resolute attention from the computing community on the protection of private information in data mining.This research involves the understanding, analysis, and optimization of the tradeoff between privacy protection, accuracy of data mining, and system resources in privacy-preserving data mining. The methodology is to establish a solid theoretical foundation that defines the requirements for privacy protection in data mining, identifies the domain of privacy-preserving strategies, and determines the achievability of such strategies. This theoretical foundation enables the design and optimization of privacy-preserving data mining algorithms that are realistic, generic, and efficient. The research results of this project have broader impacts on the nationÕs higher education system and high-tech industries. The ability to mine private data without violating the privacy of data owners is a must for a wide variety of corporations, universities, hospitals, and government agencies. Similarly, theoretically and empirically validated means to protect privacy in data mining would benefit all privacy-concerned individuals at large. The impact of this project also extends to academia through educational efforts, including graduate and undergraduate student training, curriculum development, seminars, and outreach.