Collaborative Research: ITR: Distributed Data Mining to Protect Information Privacy

隐私问题会妨碍构建集中式数据仓库来支持数据挖掘。例如，疾病控制中心（CDC）可能希望挖掘保险公司的数据，以确定疾病爆发的趋势和模式，例如了解和预测流感流行的进展。将所有患者数据收集到一个仓库中会增加隐私泄露和滥用的机会。我们提出了一种替代方案：持有数据的各方之间的安全协作计算，产生所需的数据挖掘结果，同时可证明防止私人数据的泄露。本项目将在以下假设下实现知识发现：数据分布在多个来源，安全/隐私问题限制了数据共享；如果将数据收集到一个集中的仓库中，那么数据挖掘工具就可以识别出提供有益知识的模式或关系。已开发的技术将复制或近似集中数据挖掘的结果，并对每个站点的数据披露进行可量化的限制。目标是开发一个保护隐私的分布式计算技术工具包，可以将其组合起来解决特定的现实问题。通过简化组件组装，使其成为开发而不是研究，广泛使用保护隐私的分布式数据挖掘将变得可行。

Privacy concerns can prevent constructing a centralized data warehouse to support data mining. For example, the Centers for Disease Control (CDC) may want to mine insurance companies' data to identify trends and patterns in disease outbreaks, such as understanding and predicting the progression of a flu epidemic. Gathering all patient data into a single warehouse increases opportunities for privacy breaches and misuse. We propose an alternative: secure collaborative computing between the parties holding the data that produce the desired data mining results, while provably preventing disclosure of private data.This project will enable knowledge discovery under the following assumptions:1. data are distributed across multiple sources, with security/privacy concerns that limit data sharing, and2. if data were gathered into a centralized warehouse, data mining tools could identify patterns or relationships that give beneficial knowledge.Developed techniques will replicate or approximate the results of centralized data mining, with quantifiable limits on the disclosure of data from each site. The goal is to develop a toolkit of privacy-preserving distributed computation techniques that can be assembled to solve specific real-world problems. By simplifying component assembly so it becomes development rather than research, widespread use of privacy-preserving distributed data mining will become feasible.