Name
Abstract | ||
|---|---|---|
Pervasive computing and the increasing networking needs usually demand from publishing data without revealing sen- sible information. Among several data protection methods proposed in the literature, those based on linear regression are widely used for numerical data. However, no attempts have been made to study the effect of using more complex polynomial regression methods. In this paper, we present PoROP- k, a family of anonymizing methods able to protect a data set using polynomial regressions. We show that PoROP- k not only reduces the loss of information, but it also obtains a better level of protection compared to previous proposals based on linear regressions. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IPC.2007.70 | IPC |
Keywords | Field | DocType |
polynomial regression,increasing network,complex polynomial regression method,anonymizing method,numerical data,sible information,linear regression,better level,polynomial regression complexity,data protection method,pervasive computing,data anonymization,data protection,regression analysis,ubiquitous computing | Mobile ad hoc network,Broadcasting,Data mining,Bloom filter,Fading,Computer science,Computer network,Service provider,Bit array,Routing protocol,Zone Routing Protocol | Conference |
ISBN | Citations | PageRank |
0-7695-3006-0 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jordi Nin | 1 | 311 | 26.53 |
| Jordi Pont-Tuset | 2 | 656 | 32.22 |
| Pau Medrano-Gracia | 3 | 162 | 14.03 |
| Josep-Lluis Larriba-Pey | 4 | 245 | 21.70 |
| and Victor Muntes-Mulero | 5 | 0 | 0.34 |