ITR: Private Prediction Using Selective Models

ITR：使用选择性模型的私人预测

• 批准号: 0219560
• 负责人: Wenliang Du
• 金额: $ 22万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0219560&HistoricalAwards=false
• 关键词: ITR; Private; Prediction; Using; Selective

This research will study the SPMP (Selective Private Model-basedPrediction) problem demonstrated with the following scenario: A clientwith a private input wants to use a server's private model to makepredictions; however, neither side wants to disclose its private data toanybody. More specifically, the research will study the problem forselected models including the Hidden Markov Model, the Neural NetworkModel, the Bayesian Network Model, and the Decision Trees Model. The goalis to develop efficient and practical solutions that enable such type ofprivacy preserving prediction.The project will investigate two approaches: commodity-server approach andmultiple-server approach. The commodity-server approach uses thecommodities (data) from a third party to preserve the confidentiality ofthe private data. The multiple-server approach uses duplicate servers sono single server can learn all information about the client's data. Basedon these two approaches, various data disguising techniques will also bestudied.Efficient solutions to the SPMP problems enable model owners to providenew forms of e-commerce services while protecting customers' privateinformation. Furthermore, the results, methodologies, and the buildingblocks gained from the proposed activity can provide invaluableunderstanding and insights into the Secure Multiparty Computation (SMC)research, and help to advance and expand the areas of SMC research.---------------------------------------

这项研究将研究SPMP(基于选择性私有模型的预测)问题，该问题以以下场景为例：具有私有输入的客户端希望使用服务器的私有模型来进行预测；然而，双方都不想向任何人透露其私有数据。更具体地说，研究将研究所选模型的问题，包括隐马尔可夫模型、神经网络模型、贝叶斯网络模型和决策树模型。目标是开发有效和实用的解决方案来实现这种类型的隐私保护预测。该项目将调查两种方法：商品服务器方法和多服务器方法。商品服务器方法使用来自第三方的商品(数据)来保护私人数据的机密性。多服务器方法使用重复服务器，因此单个服务器可以获取有关客户端数据的所有信息。在这两种方法的基础上，还将研究各种数据伪装技术，有效地解决SPMP问题，使模型所有者能够在保护客户隐私信息的同时提供新形式的电子商务服务。此外，从提议的活动中获得的结果、方法和构建块可以为安全多方计算(SMC)研究提供宝贵的理解和见解，并有助于推进和扩展安全多方计算research.---------------------------------------的领域