Abstract
One important class of data perturbation methods is the class of additive data perturbation methods for masking sensitive datasets containing linear relationships among their variables. Theses methods try to maintain original linear relationships in masked datasets while preserving data security and privacy. Three important methods under this class are GADP (Muralidhar et al. 1999), IPSO (Burridge 2003) and EGADP (Muralidhar and Sarathy 2005). To facilitate the work in this important area of research, we provide brief discussion of each of the three methods along with their Matlab implementations.
| Original language | English |
|---|---|
| Title of host publication | Innovation and Knowledge Management |
| Subtitle of host publication | A Global Competitive Advantage - Proceedings of the 16th International Business Information Management Association Conference, IBIMA 2011 |
| Publisher | International Business Information Management Association, IBIMA |
| Pages | 2170-2178 |
| Number of pages | 9 |
| ISBN (Print) | 9780982148952 |
| State | Published - 2011 |
Publication series
| Name | Innovation and Knowledge Management: A Global Competitive Advantage - Proceedings of the 16th International Business Information Management Association Conference, IBIMA 2011 |
|---|---|
| Volume | 4 |
Bibliographical note
Funding Information:The work presented here was funded in part by the New Mexico State Highway and Transportation Department through the Materials Laboratory Bureau.
Keywords
- Matlab
- Perturbation methods
- Privacy preserving data mining
- Statistical disclosure limitation (SDL)
ASJC Scopus subject areas
- Business and International Management
- Management Information Systems
- Management of Technology and Innovation
- Information Systems and Management