ICES: Large: Economic Foundations of Digital Privacy

在过去的十年里，私人数据已经成为一种商品--它被收集、购买和出售，并为许多互联网和信息技术公司的主要业务做出贡献。与此同时，计算机科学家已经开发和研究了各种形式的“隐私”概念。然而，到目前为止，我们缺乏关于数字隐私经济学的理论，该项目旨在弥合这一重要差距。具体地说，该项目将开发一个理论来解决以下问题：-私人数据市场应该如何构建？如何设计一场拍卖，以适应特定于私人数据分析的问题：私人数据的买家通常希望从具有代表性的人群样本中购买；个人对其隐私的价值本身可能是一条非常敏感的信息？-其他市场应该如何构建，以适当地考虑参与者对隐私的担忧？在拍卖环境中，隐私应该如何建模？市场应该如何设计，以解决与隐私效用相关的问题？-研究经济互动需要研究学习--但隐私对代理人学习的成本是多少？不完全信息是隐私保护机制的必然结果，它如何影响参与动态互动的个人如何学习和协调，以及受干扰的测量如何影响游戏中的学习动力？如何才能既有效又私下地进行市场研究？我们对这些问题的调查将融合几个相关领域的模型和方法，包括计算机科学、经济学、算法博弈论和机器学习。这个项目直接解决了互联网时代强加给社会的最重要的紧张局势之一：关于公民个人的私人数据和潜在敏感数据的巨大社会和商业价值，与这些个人控制其数据的利益和权利之间的紧张关系。尽管这种紧张关系引起了关注和争议，但还没有全面而连贯的科学来理解它。此外，由于数据隐私背后的技术和社会因素正在发生永久变化，因此需要科学(而不仅仅是技术)。在计算机科学领域，最近引入的隐私保护计算的子领域为潜在的进步指明了方向。该项目旨在拓宽和深化这些方向。

In the last decade private data has become a commodity - it is gathered, bought and sold, and contributes to the primary business of many Internet and information technology companies. At the same time, various formalizations of the notion of "privacy" have been developed and studied by computer scientists. Nevertheless, to date we lack a theory for the economics of digital privacy, and this project aims to close this important gap.Concretely, the project will develop a theory to address the following questions:- How should a market for private data be structured? How can one design an auction that accommodates issues specific to private data analysis: that the buyer of private data often wishes to buy from a representative sample from the population; and that individuals' value for their privacy can itself be a very sensitive piece of information?- How should other markets be structured to properly account for participants' concerns about privacy? How should privacy be modeled in auction settings, and how should markets be designed to address issues relating to utility for privacy?- Studying economic interactions necessitates studying learning - but what is the cost of privacy on agent learning? How does the incomplete information that is the necessary result of privacy-preserving mechanisms affect how individuals engaged in a dynamic interaction can learn and coordinate, and how do perturbed measurements affect learning dynamics in games? How can market research be conducted both usefully and privately?Our investigation of these questions will blend models and methods from several relevant fields, including computer science, economics, algorithmic game theory and machine learning.This project directly addresses one of the most important tensions that the Internet era has thrust upon society: the tension between the tremendous societal and commercial value of private and potentially sensitive data about individual citizens, and the interests and rights of those individuals to control their data. Despite the attention and controversy this tension has evoked, there is no comprehensive and coherent science for understanding it. Furthermore, science (rather than technology alone) is required, since the technological and social factors underlying data privacy are undergoing perpetual change. Within the field of computer science, the recently introduced subfield of privacy preserving computation has pointed the way to potential advances. This project aims to both broaden and deepen these directions.