Name
Abstract | ||
|---|---|---|
The secure multi-party computation (SMC) model provides means for balancing the use and confidentiality of distributed data. This is especially important in the field of privacy-preserving data mining (PPDM). Increasing security concerns have led to a surge in work on practical secure multi-party computation protocols. However, most are only proven secure under the semi-honest model, and security under this adversary model is insufficient for many PPDM applications. SMC protocols under the malicious adversary model generally have impractically high complexities for PPDM. We propose an accountable computing (AC) framework that enables liability for privacy compromise to be assigned to the responsible party without the complexity and cost of an SMC-protocol under the malicious model. We show how to transform a circuit-based semi-honest two-party protocol into a protocol satisfying the AC-framework. The transformations are simple and efficient. At the same time, the verification phase of the transformed protocol is capable of detecting any malicious behaviors that can be prevented under the malicious model. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.datak.2007.06.014 | data and knowledge engineering |
Keywords | Field | DocType |
malicious behavior,semi-honest protocol,malicious model,secure multi-party computation,adversary model,malicious adversary model,smc protocol,ppdm application,circuit-based semi-honest two-party protocol,practical secure multi-party computation,semi-honest model,data privacy,data mining,secure multi party computation,satisfiability,protocols,secure multiparty computation | Data mining,Confidentiality,Computer science,Computer security,Adversary model,Liability,Accountability,Compromise,Secure two-party computation,Information privacy,Computation | Journal |
Volume | Issue | ISSN |
65 | 1 | 0169-023X |
ISBN | Citations | PageRank |
0-7695-2702-7 | 29 | 1.19 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Jiang | 1 | 383 | 27.56 |
| Chris Clifton | 2 | 3327 | 544.44 |
| Murat Kantarcıoğlu | 3 | 29 | 1.19 |