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A Novel Approach to Preserve the Privacy of Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Mary, A. Viji Amutha |
| Copyright Year | 2014 |
| Abstract | A widely studied perturbation-based PPDM approach introduces random perturbation to individual values to conserve privacy before data are published. The data owner before publishing the data first perform changes to the data into n number of copies based on the access privilege and publishes. In that the high privilege data contains a smaller amount noise and low privilege data contains more additional noise. But the formal attackers have possibility to make diversity attack to rebuild the data using non-linear techniques. To overcome the problem, a novel approach has been introduced where the data owner after performing modification finds the noise of every copy and compare. If the noise is similar means there is no privacy in the modified data and the attacker have possibility to reconstruct the information. So the data owner adds further noise until there is no similarity. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijcsit.com/docs/Volume%205/vol5issue01/ijcsit20140501159.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
