Name
Abstract | ||
|---|---|---|
Many data mining applications deal with large data sets that contain private information that must be protected. This has led to the development of many privacy-preserving data mining techniques. Many of these techniques use randomized data distortion by adding noise to the sensitive data. However, non-careful noise addition may introduce biases to the statistical parameters of these data, including means and variances. To meet privacy requirements and preserve the statistical properties of the sensitive data we use a data transformation technique called Rotation-Based Transformation (RBT). This method distorts only confidential numerical attributes and preserves the statistical properties of the data. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1167350.1167419 | ACM Southeast Regional Conference (1) |
Keywords | Field | DocType |
statistical property,rotational data transformation,confidential numerical attribute,large data set,sensitive data,privacy-preserving mining,privacy-preserving data mining technique,non-careful noise addition,data transformation technique,randomized data distortion,data mining applications deal,statistical parameter,data transformation,data mining,private information,privacy | Statistical parameter,Data mining,Data set,Data stream mining,Data quality,Confidentiality,Computer science,Data pre-processing,Private information retrieval,Distortion | Conference |
ISBN | Citations | PageRank |
1-59593-059-0 | 3 | 0.41 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Ketel | 1 | 13 | 6.84 |
| Abdollah Homaifar | 2 | 169 | 41.12 |