TWC: Medium: Collaborative: Re[DP]: Realistic Data Mining Under Differential Privacy

个人数据的收集和分析彻底改变了信息系统，推动了美国和全球经济的发展。但对使用此类数据的隐私担忧凸显出来。差分隐私已经成为数学上描述使用个人数据的算法的隐私风险的黄金标准。 然而，在行业或政府机构中采用差异化隐私算法的情况非常罕见。 这种采用的失败主要源于隐私研究人员迄今为止所考虑的理想化问题设置与挖掘个人数据所需的复杂现实工作流程之间的不匹配。 该项目将扩大隐私算法的实际用途，通过向美国人口普查和杜克大学的医学研究人员转让技术来鼓励其使用，并最终通过增加数据共享和知识传输来确保隐私保护。该项目旨在系统地研究挖掘个人数据所涉及的完整工作流程，并解决降低可用性和阻止差异隐私的广泛部署的关键问题。研究活动包括开发（i）用于数据预处理（清洗，插补和其他转换）的私有算法，（ii）支持并行和迭代模型选择的算法，（iii）用于设置隐私策略和实用程序基准的语义有意义的指南。 结果将指导设计和实施一种新的基于Web的框架（DPcomp）的测试和评估部署的隐私算法。 该项目更广泛的影响包括向美国人口普查和杜克大学的医学研究人员转让技术，并将隐私主题纳入新的本科课程。DPcomp将促进数据所有者和隐私研究人员之间的互动，并帮助挖掘新的研究问题。

The collection and analysis of personal data about individuals has revolutionized information systems and fueled US and global economies. But privacy concerns regarding the use of such data loom large. Differential privacy has emerged as a gold standard for mathematically characterizing the privacy risks of algorithms using personal data. Yet, adoption of differentially private algorithms in industry or government agencies has been startlingly rare. This failure of adoption stems largely from a mismatch between the idealized problem settings considered to date by privacy researchers and the complex real-world workflows needed for mining personal data. This project will expand the practical usefulness of privacy algorithms, encouraging their use through technology transfer to the US Census and medical researchers at Duke University, and ultimately ensuring privacy protection with increased data sharing and transmission of knowledge.This project aims to systematically study the complete workflow involved in mining personal data, and solve key problems that have diminished usability and prevented widespread deployment of differential privacy. Research activities include developing (i) private algorithms for data preprocessing (cleaning, imputation, and other transformations), (ii) algorithms to support parallel and iterative model selection, (iii) semantically meaningful guidelines for setting privacy policies and utility benchmarks. Results will guide the design and implementation of a novel web-based framework (DPcomp) for testing and evaluating the deployment of privacy algorithms. Broader impacts of this project include technology transfer to the US Census and medical researchers at Duke University, and incorporating privacy themes into new undergraduate courses. DPcomp will stimulate interaction between data owners and privacy researchers, and help unearth new research questions.