Name
Abstract | ||
|---|---|---|
While progress has been made on techniques, tools and systems for providing data privacy, there is no general methodology for determining the extent to which these techniques, tools and systems reduce practical privacy risks. We need a comprehensive framework where the privacy and utility of multiple privacy-preserving techniques could be measured. This vision paper provides directions for designing such a framework. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BigDataCongress.2017.92 | 2017 IEEE International Congress on Big Data (BigData Congress) |
Keywords | Field | DocType |
cyber privacy metrics,data privacy,privacy-preserving techniques | Data mining,Internet privacy,Privacy by Design,Computer security,Cryptography,Computer science,Information privacy,Privacy software | Conference |
ISSN | ISBN | Citations |
2379-7703 | 978-1-5386-1997-1 | 0 |
PageRank | References | Authors |
0.34 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bhavani M. Thuraisingham | 1 | 2587 | 282.14 |
| Murat Kantarcioglu | 2 | 2470 | 168.03 |
| Elisa Bertino | 3 | 14025 | 2128.50 |
| Chris Clifton | 4 | 3327 | 544.44 |