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Privacy Protection Using Generalization For Collaborative Data Publishing
| Content Provider | Semantic Scholar |
|---|---|
| Author | Saidireddy, Malgireddy |
| Copyright Year | 2018 |
| Abstract | Organizations share their data about customers for exploring potential business avenues. The sharing of data has posed several threats leading to individual identification. Owing to this, privacy preserving data publication has become an important research problem. The main goals of this problem are to preserve privacy of individuals while revealing useful information. An organization may implement and follow its privacy policy. But when two companies share information about a common set of individuals, and if their privacy policies differ, it is likely that there is privacy breach unless there is a common policy. One such solution was proposed for such a scenario, based on k-anonymity and cut-tree method for 2-party data. This paper suggests a simple solution for integrating n-party data using dynamic programming on subsets. The solution is based on thresholds for privacy and in formativeness based on k-anonymity. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ijcrt.org/papers/IJCRTNCES061.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
