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Privacy-preserving Multiparty Collaborative Mining with Geometric Data Perturbation
| Content Provider | CiteSeerX |
|---|---|
| Author | Chen, Keke Liu, Ling |
| Abstract | Abstract—In multiparty collaborative data mining, participants contribute their own datasets and hope to collaboratively mine a comprehensive model based on the pooled dataset. How to efficiently mine a quality model without breaching each party’s privacy is the major challenge. In this paper, we propose an approach based on geometric data perturbation and datamining-service oriented framework. The key problem of applying geometric data perturbation in multiparty collaborative mining is to securely unify multiple geometric perturbations that are preferred by different parties, respectively. We have developed three protocols for perturbation unification. Our approach has three unique features compared to the existing approaches. (1) With geometric data perturbation, these protocols can work for many existing popular data mining algorithms, while most of other approaches are only designed for a particular mining algorithm. (2) Both the two major factors: data utility and privacy guarantee are well preserved, compared to other perturbationbased approaches. (3) Two of the three proposed protocols also have great scalability in terms of the number of participants, while many existing cryptographic approaches consider only two or a few more participants. We also study different features of the three protocols and show the advantages of different protocols in experiments. Index Terms—privacy preserving data mining, distributed computing, collaborative computing, geometric data perturbation I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Perturbation Unification Multiparty Collaborative Mining Data Mining Quality Model Different Party Privacy-preserving Multiparty Collaborative Mining Great Scalability Party Privacy Comprehensive Model Different Feature Geometric Data Perturbation Unify Multiple Geometric Perturbation Particular Mining Algorithm Perturbationbased Approach Key Problem Collaborative Computing Privacy Guarantee Data Utility Major Factor Pooled Dataset Popular Data Mining Algorithm Index Term Unique Feature Multiparty Collaborative Data Mining Cryptographic Approach Different Protocol |
| Content Type | Text |
