TY - GEN
T1 - Privacy preserving nearest neighbor search
AU - Shaneck, Mark
AU - Kim, Yongdae
AU - Kumar, Vipin
PY - 2006
Y1 - 2006
N2 - 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 multi-party 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.
AB - 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 multi-party 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.
UR - http://www.scopus.com/inward/record.url?scp=51849103902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51849103902&partnerID=8YFLogxK
U2 - 10.1109/icdmw.2006.133
DO - 10.1109/icdmw.2006.133
M3 - Conference contribution
AN - SCOPUS:51849103902
SN - 0769527027
SN - 9780769527024
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 541
EP - 545
BT - Proceedings - ICDM Workshops 2006 - 6th IEEE International Conference on Data Mining - Workshops
PB - Institute of Electrical and Electronics Engineers Inc.
ER -