Name
Abstract | ||
|---|---|---|
In this paper, we initiate the systematic study of solving linear programs under differential privacy. The first step is simply to define the problem: to this end, we introduce several natural classes of private linear programs that capture different ways sensitive data can be incorporated into a linear program. For each class of linear programs we give an efficient, differentially private solver based on the multiplicative weights framework, or we give an impossibility result. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-43948-7_51 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Journal | 8572 | 0302-9743 |
Citations | PageRank | References |
10 | 0.58 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin Hsu | 1 | 364 | 24.38 |
| Aaron Roth | 2 | 1937 | 110.48 |
| Tim Roughgarden | 3 | 4177 | 353.32 |
| Jonathan Ullman | 4 | 485 | 40.07 |