Name
Abstract | ||
|---|---|---|
Privacy-preserving data mining has concentrated on obtaining valid results when the input data is private. An extreme example is Secure Multiparty Computation-based methods, where only the results are revealed. However, this still leaves a potential privacy breach: Do the results themselves violate privacy? This paper explores this issue, developing a framework under which this question can be addressed. Metrics are proposed, along with analysis that those metrics are consistent in the face of apparent problems. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1145/1014052.1014126 | KDD |
Keywords | Field | DocType |
extreme example,valid result,input data,apparent problem,secure multiparty computation-based method,privacy-preserving data mining,potential privacy breach,data mining result,secure multiparty computation,data mining,privacy | Data mining,Internet privacy,Secure multi-party computation,Computer science,Computer security,Inference,Information privacy,Privacy software | Conference |
ISBN | Citations | PageRank |
1-58113-888-1 | 73 | 2.92 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Murat Kantarcioglu | 1 | 2470 | 168.03 |
| Jiashun Jin | 2 | 114 | 7.75 |
| Chris Clifton | 3 | 3327 | 544.44 |