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Differentially Private Data Release for Data Mining (2011)
| Content Provider | CiteSeerX |
|---|---|
| Author | Mohammed, Noman Chen, Rui Fung, Benjamin C. M. Yu, Philip S. |
| Description | Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful infor-mation. Among the existing privacy models, -differential privacy provides one of the strongest privacy guarantees and has no assumptions about an adversary’s background knowledge. Most of the existing solutions that ensure -differential privacy are based on an interactive model, where the data miner is only allowed to pose aggregate queries to the database. In this paper, we propose the first anonymiza-tion algorithm for the non-interactive setting based on the generalization technique. The proposed solution first prob-abilistically generalizes the raw data and then adds noise to guarantee -differential privacy. As a sample application, we show that the anonymized data can be used effectively to build a decision tree induction classifier. Experimen-tal results demonstrate that the proposed non-interactive anonymization algorithm is scalable and performs better than the existing solutions for classification analysis. |
| File Format | |
| Language | English |
| Publisher | ACM |
| Publisher Date | 2011-01-01 |
| Publisher Institution | Proc. KDD 11 |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
