Name
Abstract | ||
|---|---|---|
Computing a differentially private function using secure function evaluation prevents private information leakage both in the process, and from information present in the function output. However, the very secrecy provided by secure function evaluation poses new challenges if any of the parties are malicious. We first show how to build a two party differentially private secure protocol in the presence of malicious adversaries. We then relax the utility requirement of computational differential privacy to reduce computational cost, still giving security with rational adversaries. Finally, we provide a modified two-party computational differential privacy definition and show correctness and security guarantees in the rational setting. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/PST.2015.7232954 | 2015 13th Annual Conference on Privacy, Security and Trust (PST) |
Keywords | Field | DocType |
Laplace noise generation,differentially private function,secure function evaluation,private information leakage,private secure protocol,malicious adversary,utility requirement,computational cost,two-party computational differential privacy definition,security guarantee | Internet privacy,Differential privacy,Laplace transform,Computer security,Computer science,Secrecy,Correctness,Encryption,Hamming distance,Private information retrieval,Privacy software | Conference |
ISSN | Citations | PageRank |
1712-364X | 1 | 0.36 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Balamurugan Anandan | 1 | 28 | 2.35 |
| Chris Clifton | 2 | 3327 | 544.44 |