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

职业：隐私保护数据挖掘的可实现性和优化的理论基础

• 批准号: 0747150
• 负责人: Nan Zhang
• 金额: $ 44.09万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-01-01 至 2008-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0747150&HistoricalAwards=false
• 关键词: CAREER; Theoretical; Foundation; Achievability; Optimization

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.

职业：在隐私数据挖掘中的实现和优化的理论已成功地应用于各种应用程序，包括营销，天气预报，医疗诊断和国土安全性。关键的挑战。G隐私立法，例如《保险和问责法》（HIPAA）对隐私保护的关注，需要对Protectecte Information Da Ta Mining的共同点进行IMEDINTY ATSERIAN。这项研究涉及隐私保护之间的权衡隐私数据挖掘中的系统资源。现实，通用和效率对国家教育系统和高科技行业的影响。经过验证的数据挖掘中的隐私权将使所有受到隐私的人都受益。