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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shaneck, M. Yongdae Kim Kumar, V. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Comput. Sci., Minnesota Univ., Minneapolis, MN (Shaneck, M.; Yongdae Kim; Kumar, V.) |
| Abstract | Data mining is frequently obstructed by privacy concerns. In many cases data is distributed, and bringing the data together in one place for analysis is not possible due to privacy laws (e.g. HIPAA) or policies. Privacy preserving data mining techniques have been developed to address this issue by providing mechanisms to mine the data while giving certain privacy guarantees. In this work we address the issue of privacy preserving nearest neighbor search, which forms the kernel of many data mining applications. To this end, we present a novel algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data. We show how this algorithm can be used in three important data mining algorithms, namely LOF outlier detection, SNN clustering, and kNN classification |
| Sponsorship | IEEE CPS |
| Starting Page | 541 |
| Ending Page | 545 |
| File Size | 128421 |
| Page Count | 5 |
| File Format | |
| ISBN | 0769527027 |
| DOI | 10.1109/ICDMW.2006.133 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer science Data privacy Conferences Clustering algorithms Data mining Cryptography Distributed computing Kernel Medical diagnostic imaging Nearest neighbor searches |
| Content Type | Text |
| Resource Type | Article |
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