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、科学研究等领域发挥着越来越关键的作用，私人数据的商品化一直在上升。人们越来越难知道数据可能被如何使用，也越来越难保持对自己数据的控制。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。随着模糊的隐私政策和幕后数据经纪市场成为常态，这种模式正变得站不住脚。在实践中，有两个基本问题需要解决：(I)数据主体在将私人数据转移给数据收集者后无法控制数据隐私；(Ii)数据收集者拥有保护用户私人数据的唯一能力。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，数据收集者提供激励以换取获得更准确的数据。这项研究将使私人数据收集的传统做法从知情同意转变为基于市场的方法，其中数据收集者只拥有所需数据的保真度，从而减少数据泄露的潜在损害，并使数据主体更好地控制其私人数据的使用。特别是，所考虑的问题是在博弈论的背景下研究的，对于一般的私人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，在成本和准确性之间的权衡；从数据主体的角度，在报酬和隐私之间的权衡。研究任务包括(I)设计有效的激励机制，让数据收集者以最低的成本收集高质量的数据(由个人控制)；以及(Ii)开发保护私人的报告算法，通过同时考虑支付和隐私损失来最大化数据对象的收益。在这个项目中开发的新理论和机制将被整合到本科和研究生课程中。关于这个项目的更多信息可以在项目主页http://inlab.lab.asu.edu/data-privacy/上找到

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/