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、蜂窝网络中的移动轨迹、搜索日志和电影评级等形式不断积累在各种数据库中，仅举几例。这些数据集有许多有价值的用途，但很难在保护隐私的同时实现这些用途。即使当数据收集者试图通过发布匿名或聚合的数据来保护客户的隐私时，这些数据也经常显示出比预期更多的信息。为了可靠地防止这种隐私侵犯，我们需要用一个有原则的数据发布机制来取代当前的临时解决方案，该机制提供强大的、可证明的隐私保证。最近对差分隐私的研究使我们离实现这一目标又近了一大步。差异隐私使我们能够正式地推断对手可以从发布的数据中了解到什么，同时避免了许多假设的需要（例如，对手可能已经知道什么），这些假设的失败在过去一直是隐私侵犯的原因。然而，尽管差分隐私有很大的前景，但在实践中仍然很少使用。证明一个给定的计算可以以一种不同的私有方式执行，需要该领域的专家付出大量的人工努力，这阻碍了它在实践中的扩展。该项目旨在将差异隐私付诸实践——构建一个支持差异隐私数据分析的系统，它可以被普通程序员使用，并且足以用于各种各样的应用程序。这样的系统可以广泛使用，并且在任何发布或分析敏感数据的地方都可以提供强大的隐私保障。具体贡献将包括丰富差分隐私的基本模型，以解决实际问题，如具有固有相关性的数据，提高准确性，功能隐私或流数据隐私；开发一种差分私有编程语言，以及一个可以自动证明该语言的程序是差分私有的编译器，以及一个针对侧信道攻击的强化运行时系统；并展示了如何在分布式环境中应用差异隐私，在这种环境中，私有数据分布在不同管理域的许多数据库中，可能存在重叠、异构模式和不同的隐私期望。长期目标是将差分隐私、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。差别隐私的主题也被整合到宾夕法尼亚大学市场与社会系统工程的新本科课程中。

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.