CAREER: Collaborative Optimization with Limited Information Disclosure

职业：有限信息披露的协作优化

• 批准号: 0746943
• 负责人: Jaideep Vaidya
• 金额: $ 45万
• 依托单位: Rutgers University Newark
• 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: 2008-02-01 至 2013-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746943&HistoricalAwards=false
• 关键词: CAREER; Collaborative; Optimization; Limited; Information

CAREER: Collaborative Optimization with Limited Information DisclosureWith the rapid increase in computing, storage and networking resources, data is not only collected and stored but also analyzed. This creates a serious privacy problem which often inhibits the use of this data. This project explores the problem of performing optimization analysis over distributed data without conflicting with privacy and security concerns. This is especially challenging due to the complexity and iterative nature of the solutions. An inherent aim is to also solve some of the fundamental problems underlying privacy-preserving analysis / secure computation and make it more accessible and applicable. Some of the innovative expected results include: (1) novel formulations of security definitions that are more relaxed than the traditional definitions yet still model the real security concerns; (2) new algorithms, computational complexity results, and tools for specific widely used optimization problems; (3) a more generalized view of privacy; (4) game theoretic interpretations and modeling of the multi-party computation; and (5) result analysis ? a quantification of privacy loss through results. The project will have tremendous broader impact via fundamental research and integrative education. Direct outcomes of the research can significantly help in widening co-operation between organizations and minimize loss through data isolation. This would result in cost savings and new income realization potentially worth billions of dollars through joint resource usage. Translation of the research to real use has the potential to revolutionize the mediator/consolidator industry. The integrative education activities will foster actual use of the technology and open up its acceptance into the real world

职业：信息披露受限的协同优化随着计算、存储和网络资源的快速增长，数据不仅要被收集和存储，还要被分析。这造成了严重的隐私问题，往往会阻碍这些数据的使用。该项目探讨了在不与隐私和安全问题冲突的情况下对分布式数据进行优化分析的问题。由于解决方案的复杂性和迭代性，这尤其具有挑战性。一个内在的目标是解决隐私保护分析/安全计算背后的一些基本问题，并使其更容易获得和适用。一些创新性的预期结果包括：（1）新的安全定义的公式，比传统的定义更宽松，但仍然模拟真实的安全问题;（2）新的算法，计算复杂性的结果，和工具，为特定的广泛使用的优化问题;（3）更广义的隐私观;（4）博弈论的解释和多方计算的建模;（5）结果分析。通过结果量化隐私损失。该项目将通过基础研究和综合教育产生巨大的更广泛的影响。研究的直接成果可以大大有助于扩大组织之间的合作，并通过数据隔离将损失降至最低。这将通过联合使用资源节省成本，实现价值数十亿美元的新收入。将研究转化为真实的使用有可能彻底改变中介/整合行业。综合教育活动将促进技术的实际使用，并使其进入真实的世界