Name
Abstract | ||
|---|---|---|
Differential Privacy (DP) has emerged as a formal, flexible framework for privacy protection, with a guarantee that is agnostic to auxiliary information and that admits simple rules for composition. Benefits notwithstanding, a major drawback of DP is that it provides noisy responses to queries, making it unsuitable for many applications. We propose a new notion called Noiseless Privacy that provides exact answers to queries, without adding any noise whatsoever. While the form of our guarantee is similar to DP, where the privacy comes from is very different, based on statistical assumptions on the data and on restrictions to the auxiliary information available to the adversary. We present a first set of results for Noiseless Privacy of arbitrary Boolean-function queries and of linear Real-function queries, when data are drawn independently, from nearly-uniform and Gaussian distributions respectively. We also derive simple rules for composition under models of dynamically changing data. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25385-0_12 | ASIACRYPT |
Keywords | DocType | Volume |
differential privacy,arbitrary boolean-function query,noiseless database privacy,simple rule,privacy protection,noiseless privacy,gaussian distribution,derive simple rule,flexible framework,auxiliary information,exact answer,boolean function | Journal | 2011 |
ISSN | Citations | PageRank |
0302-9743 | 13 | 0.62 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raghav Bhaskar | 1 | 191 | 9.88 |
| Abhishek Bhowmick | 2 | 26 | 2.53 |
| Vipul Goyal | 3 | 2859 | 129.53 |
| Srivatsan Laxman | 4 | 421 | 21.65 |
| Abhradeep Thakurta | 5 | 500 | 28.18 |