We have another postdoc position available at Penn for theorists interested in studying the foundations of privacy, and developing a theory of how privacy and economic incentives should interact!
Applications are invited for a postdoc position in the theory of privacy and economics at the University of Pennsylvania. An outline of the hosting project is below.
The ideal candidate will have a Ph.D. in Computer Science, Economics, or Statistics and a strong record of publication. To apply, please send a CV, research statement, and the names of three people who can be asked for letters of reference to Aaron Roth (aaroth@cis.upenn.edu). Both the term of the postdoc and the starting date are negotiable.
Inquiries can be directed to any of the PIs:
Sham Kakade
Michael Kearns
Mallesh Pai
Aaron Roth
---
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 we propose to close this important gap.
Concretely, we propose to develop the theory to address the following questions:
How should a market for private data be structured? How can we 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 we structure other markets to properly account for participants concerns about privacy? How should we properly model privacy in auction settings, and design markets 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.
The proposed research 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, we lack a 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. The proposed research aims to both broaden and deepen these directions.
