|
Activity Number:
|
193
|
|
Type:
|
Invited
|
|
Date/Time:
|
Monday, August 3, 2009 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Survey Research Methods
|
| Abstract - #302927 |
|
Title:
|
Integrating Differential Privacy with Statistical Theory
|
|
Author(s):
|
Adam D. Smith*+
|
|
Companies:
|
Penn State University
|
|
Address:
|
338K I.S.T. Building, University Park, PA, 16802,
|
|
Keywords:
|
|
|
Abstract:
|
This talk discusses recent results on designing differential private analytical methods that are asymptotically efficient, meaning that they produces answers (estimates) that converge to the correct value at the same rate as the optimal estimator, as the number of sample points increases. These results show a range of settings in which privacy is achieved at no (asymptotic) cost. They provide further evidence that rigorous definitions of privacy are compatible with valid statistical inference.
|
- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
Back to the full JSM 2009 program |
