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

TWC SBE：小型：迈向隐私保护数据分析的经济基础：激励机制和基本限制

基本信息

批准号：
1618768
负责人：
Junshan Zhang
金额：
$ 50万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618768&HistoricalAwards=false
关键词：
TWC SBE Small Towards Economic

项目摘要

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/

私人数据的商品化一直在趋势，因为大数据分析在广告，科学研究等中起着更为关键的作用。知道如何使用数据或保留对自己的数据的控制越来越困难。收集私人数据的一种常见做法是基于“知情同意书”，在该数据主体（个人）决定是否报告数据，基于谁在收集数据，收集的数据以及如何使用数据。这种模式变得站不住脚，具有模糊的隐私政策，并且幕后数据经纪市场成为常态。实际上，需要解决两个基本问题：（i）将私人数据传输到数据收集器后，数据主体无法控制数据隐私；（ii）数据收集器具有保护用户私人数据的唯一能力。该项目采用一种新的，基于市场的方法：数据主体通过报告嘈杂的数据来控制自己的数据隐私，并且数据收集器提供了激励措施，以换取接收更准确的数据。这项研究将使范式从传统的知情同意习惯转变为私人数据收集的实践转变为基于市场的方法，在这种方法中，数据收集器仅具有所需的数据保真度，从而减少了数据泄露的潜在损害，并为数据主体提供了更大的控制权。特别是，在游戏理论环境中，针对一般私人数据模型和各种隐私概念中研究了所考虑的问题，重点是量化两个基本的权衡：成本和准确性之间的权衡从数据主体的角度来看，数据收集器的观点以及奖励与隐私之间的权衡。研究任务包括（i）为数据收集者设计有效的激励机制，以收集最低成本的质量数据（由个人控制）；（ii）开发私人保护算法，通过考虑付款和隐私损失，从而最大程度地提高了数据主体的收益。该项目中开发的新理论和机制将集成到本科和研究生课程中。有关此项目的更多信息，请参见项目主页http://inlab.lab.asu.edu/data-privacy/