| Sarah's Tech & Research Blog
Our work is aimed at better understanding the behavior of agents in settings where their privacy concerns are explicitly given. We consider a toy setting where agent A, in an attempt to discover the secret type of agent B, offers B a gift that one type of B agent likes and the other type dislikes.
In this work, we propose a new, general way of modeling privacy in players’ utility functions. Specifically, we only assume that if an outcome o has the property that any report of player i would have led to o with approximately the same probability, then o has a small privacy cost to player i.
In the area of privacy-preserving data mining, a differentially private mechanism intuitively encourages people to share their data because they are at little risk of revealing their own information.
Towards constructing mechanisms for privacy-aware agents, we put forward and justify a model of privacy-aware mechanism design. We then show that privacy-aware mechanisms are feasible.
We initiate the study of markets for private data, through the lens of differential privacy. Although the purchase and sale of private data has already begun on a large scale, a theory of privacy as a commodity is missing.
This paper explores the role of privacy-preserving algorithms in mechanism design, specifically using differential privacy to ensure that participants have limited effect on the outcome of the mechanism and limited incentive to lie.
I describe the characteristics of scoring rules.
I explain the concept and privacy objectives of PPSM for processing sensitive data in electricity or gas markets.
There is no excerpt because this is a protected post.
There is no excerpt because this is a protected post.