TC: Medium: Putting Differential Privacy To Work

关于个人的大量数据以医疗记录、社交网络图表、蜂窝网络中的移动痕迹、搜索日志和电影收视率的形式不断积累在各种数据库中，仅举几例。这类数据集有许多有价值的用途，但在保护隐私的同时实现这些用途是困难的。即使数据收集者试图通过发布匿名或聚合数据来保护客户的隐私，这些数据通常也会泄露比预期多得多的信息。为了可靠地防止这种侵犯隐私的行为，我们需要用一种原则性的数据发布机制来取代目前的特别解决方案，该机制提供了强有力的、可证明的隐私保障。最近对差异隐私的研究使我们离实现这一目标又近了一大步。差异化隐私允许我们正式推理对手可以从发布的数据中了解到什么，同时避免需要许多假设(例如，关于对手可能已经知道的内容)，这些假设的失败在过去一直是侵犯隐私的原因。然而，尽管有很大的希望，差异隐私在实践中仍然很少使用。要证明给定的计算可以以不同的私有方式执行，该领域的专家需要大量的人工工作，这阻碍了它在实践中的扩展。此项目旨在将差异隐私付诸实施-构建一个支持差异隐私数据分析的系统，该系统可供普通程序员使用，并且足够通用，可用于各种应用程序。这样的系统可以广泛使用，在发布或分析敏感数据的任何地方，都可以将强大的隐私保证作为标准功能。具体贡献将包括丰富差异隐私的基本模型，以解决实际问题，如具有内在相关性的数据、提高的准确性、函数的隐私或流数据的隐私；开发一种差异隐私编程语言，以及一个可以自动证明该语言的程序是差异隐私的编译器，以及一个加强了对旁路攻击的运行时系统；以及展示如何在分布式环境中应用差异隐私，在分布式环境中，私有数据分散在不同管理域的许多数据库中，可能存在重叠、异类模式和不同的隐私期望。长期目标是将差异隐私、编程语言和分布式系统的思想结合起来，使具有强大的、可证明的隐私保证的数据分析技术适用于一般用途。差异隐私的主题也被整合到宾夕法尼亚大学新的市场和社会系统工程本科课程中。

A wealth of data about individuals is constantly accumulating in various databases in the form of medical records, social network graphs, mobility traces in cellular networks, search logs, and movie ratings, to name only a few. There are many valuable uses for such datasets, but it is difficult to realize these uses while protecting privacy. Even when data collectors try to protect the privacy of their customers by releasing anonymized or aggregated data, this data often reveals much more information than intended. To reliably prevent such privacy violations, we need to replace the current ad-hoc solutions with a principled data release mechanism that offers strong, provable privacy guarantees. Recent research on DIFFERENTIAL PRIVACY has brought us a big step closer to achieving this goal. Differential privacy allows us to reason formally about what an adversary could learn from released data, while avoiding the need for many assumptions (e.g. about what an adversary might already know), the failure of which have been the cause of privacy violations in the past. However, despite its great promise, differential privacy is still rarely used in practice. Proving that a given computation can be performed in a differentially private way requires substantial manual effort by experts in the field, which prevents it from scaling in practice. This project aims to put differential privacy to work---to build a system that supports differentially private data analysis, can be used by the average programmer, and is general enough to be used in a wide variety of applications. Such a system could be used pervasively and make strong privacy guarantees a standard feature wherever sensitive data is being released or analyzed. Specific contributions will include ENRICHING THE FUNDAMENTAL MODEL OF DIFFERENTIAL PRIVACY to address practical issues such as data with inherent correlations, increased accuracy, privacy of functions, or privacy for streaming data; DEVELOPING A DIFFERENTIALLY PRIVATE PROGRAMMING LANGUAGE, along with a compiler that can automatically prove programs in this language to be differentially private, and a runtime system that is hardened against side-channel attacks; and SHOWING HOW TO APPLY DIFFERENTIAL PRIVACY IN A DISTRIBUTED SETTING in which the private data is spread across many databases in different administrative domains, with possible overlaps, heterogeneous schemata, and different expectations of privacy. The long-term goal is to combine ideas from differential privacy, programming languages, and distributed systems to make data analysis techniques with strong, provable privacy guarantees practical for general use. The themes of differential privacy are also being integrated into Penn's new undergraduate curriculum on Market and Social Systems Engineering.