TWC SBE: Small: Towards an Economic Foundation of Privacy-Preserving Data Analytics: Incentive Mechanisms and Fundamental Limits

随着大数据分析在广告、科学研究等方面发挥着越来越重要的作用，私人数据的商品化趋势一直在上升。 收集私人数据的一种常见做法是基于“知情同意”，即数据主体（个人）根据谁在收集数据、收集什么数据以及如何使用数据来决定是否报告数据。 这种模式正在变得站不住脚，模糊的隐私政策和幕后数据经纪市场正在成为常态。 实际上，有两个基本问题需要解决：（i）数据当事人在将私人数据转移给数据收集者后，无法控制数据隐私;以及（ii）数据收集者有唯一能力保护用户的私人数据。该项目采用了一种新的、基于市场的方法：数据主体通过报告噪声数据来控制自己的数据隐私，数据收集者提供奖励以换取更准确的数据。 这项研究将使一个范式的转变，从传统的做法知情同意的私人数据收集，以市场为基础的方法，数据收集者只有所需的数据的保真度，减少潜在的损害，从数据泄露，并给予数据当事人更大的控制使用其私人数据。特别是，正在考虑的问题是在博弈论的设置，对于一般的私有数据模型和各种隐私概念，重点是量化两个基本的权衡：从数据收集者的角度来看，成本和准确性之间的权衡，以及从数据主体的角度来看，奖励和隐私之间的权衡。研究工作包括：（i）设计有效的激励机制，鼓励数据收集者以最低成本收集高质量的数据（由个人控制）;以及（ii）开发保护隐私的报告算法，通过考虑支付和隐私损失，最大限度地提高数据主体的回报。本项目中开发的新理论和机制将被整合到本科生和研究生课程中。有关本项目的更多信息，请访问项目主页http://inlab.lab.asu.edu/data-privacy/

The commoditization of private data has been trending up, as big data analytics is playing a more critical role in advertising, scientific research, etc. It is becoming increasingly difficult to know how data may be used, or to retain control over data about oneself. One common practice of collecting private data is based on "informed consent", where data subjects (individuals) decide whether to report data or not, based upon who is collecting the data, what data is collected, and how the data will be used. This model is becoming untenable, with vague privacy policies and a behind-the-scenes data brokerage market becoming the norm. In practice, there are two fundamental issues that need to be addressed: (i) data subjects have no control of data privacy after transferring private data to the data collector; and (ii) the data collector has sole ability to protect users' private data. This project takes a new, market-based approach: data subjects control their own data privacy by reporting noisy data, and data collectors provide incentives in exchange for receiving more accurate data. This research will enable a paradigm shift from the traditional practice of informed consent for private data collection to a market-based approach where data collectors have only the fidelity of data needed, reducing the potential damage from data breach and giving data subjects greater control over use of their private data.In particular, the problem under consideration is studied in a game-theoretic setting, for general private data models and for a variety of privacy notions, with focus on quantifying two fundamental tradeoffs: the tradeoff between cost and accuracy from the data collector's perspective, and the tradeoff between reward and privacy from a data subject's perspective. The research tasks include (i) devising effective incentive mechanisms for data collectors to collect quality data (controlled by individuals) with minimum cost; and (ii) developing private-preserving reporting algorithms that maximize data subjects' payoffs by taking both payment and privacy loss into account. New theories and mechanisms developed in this project will be integrated into undergraduate and graduate courses.More information about this project can be found at the project homepage http://inlab.lab.asu.edu/data-privacy/