| Title | Epsilon Voting: Mechanism Design for Parameter Selection in Differential Privacy |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Kohli, Nitin, Laskowski, Paul |
| Conference Name | 2018 IEEE Symposium on Privacy-Aware Computing (PAC) |
| Date Published | sep |
| Keywords | Aggregates, Companies, Computational modeling, Computing Theory and Privacy, data privacy, Differential privacy, differentially private system, epsilon voting, game theory, Human Behavior, mechanism design, parameter epsilon, parameter selection, privacy, pubcrawl, Resiliency, Scalability, Standards, Statistics, user preferences |
| Abstract | The behavior of a differentially private system is governed by a parameter epsilon which sets a balance between protecting the privacy of individuals and returning accurate results. While a system owner may use a number of heuristics to select epsilon, existing techniques may be unresponsive to the needs of the users who's data is at risk. A promising alternative is to allow users to express their preferences for epsilon. In a system we call epsilon voting, users report the parameter values they want to a chooser mechanism, which aggregates them into a single value. We apply techniques from mechanism design to ask whether such a chooser mechanism can itself be truthful, private, anonymous, and also responsive to users. Without imposing restrictions on user preferences, the only feasible mechanisms belong to a class we call randomized dictatorships with phantoms. This is a restrictive class in which at most one user has any effect on the chosen epsilon. On the other hand, when users exhibit single-peaked preferences, a broader class of mechanisms - ones that generalize the median and other order statistics - becomes possible. |
| DOI | 10.1109/PAC.2018.00009 |
| Citation Key | kohli_epsilon_2018 |
Epsilon Voting: Mechanism Design for Parameter Selection in Differential Privacy