Name
Abstract | ||
|---|---|---|
We advance the approach initiated by Chawla et al. for sanitizing (census) data so as to preserve the privacy of respon- dents while simultaneously extracting "useful" statistical information. First, we extend the scope of their techniques to a broad and rich class of distrib- utions, specifically, mixtures of high- dimensional balls, spheres, Gaussians, and other "nice" distributions. Second, we randomize the histogram construc- tions to preserve spatial characteristics of the data, allowing us to approximate various quantities of interest, e. g., cost of the minimum spanning tree on the data, in a privacy-preserving fashion. |
Year | Venue | Keywords |
|---|---|---|
2005 | Uncertainty in Artificial Intelligence | minimum spanning tree |
DocType | Volume | Citations |
Conference | abs/1207.1371 | 2 |
PageRank | References | Authors |
0.52 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuchi Chawla | 1 | 1872 | 186.94 |
| Cynthia Dwork | 2 | 9137 | 821.87 |
| Frank McSherry | 3 | 4289 | 288.94 |
| Kunal Talwar | 4 | 4423 | 259.79 |