KNG - A New Mechanism for Data Privacy
Event details
| Date | 16.10.2020 |
| Hour | 15:15 › 16:30 |
| Speaker | Matthew Reimherr, Penn State University |
| Location | |
| Category | Conferences - Seminars |
In this presentation we consider a new mechanism for achieving Differential Privacy called KNG.
Two of the most popular methods, the exponential mechanism and objective perturbation, have had great
success, but still have several drawbacks that can range from minor to severe depending on the setting.
Recently it was shown that the exponential mechanism is not asymptotically efficient, introducing too much
noise, and thus reducing statistical utility quite broadly. Conversely, objective perturbation enjoys excellent
utility, but can be difficult to generalize and requires strong structural assumptions.
We show how our new approach, KNG, assuages nearly all of these issues; it is nearly as easy to implement as the exponential
mechanism, but has much better asymptotic properties. We highlight how KNG agrees with well known mechanisms in simpler settings, while using its framework to develop new privacy tools in more complicated
settings such as linear and quantile regression.
Practical information
- Informed public
- Free
Organizer
- Prof. Victor Panaretos
Contact
- Maroussia Schaffner