Privacy Preserving Nearest Neighbor Search

Authors:
Mark Shaneck;Yongdae Kim;Vipin Kumar
Affiliations:
University of Minnesota;University of Minnesota;University of Minnesota
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

Citing 0
Cited 12

Privacy-preserving regression algorithms

SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Private queries in location based services: anonymizers are not necessary

Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A new efficient privacy-preserving scalar product protocol

AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Privacy-Preserving Distributed k-Nearest Neighbor Mining on Horizontally Partitioned Multi-Party Data

ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Practical protocol for Yao’s millionaires problem enables secure multi-party computation of metrics and efficient privacy-preserving k-NN for large data sets

Knowledge and Information Systems
Improved Garbled Circuit Building Blocks and Applications to Auctions and Computing Minima

CANS '09 Proceedings of the 8th International Conference on Cryptology and Network Security
Secure top-k subgroup discovery

PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Privacy-preserving outsourcing of brute-force key searches

Proceedings of the 3rd ACM workshop on Cloud computing security workshop
Private similarity computation in distributed systems: from cryptography to differential privacy

OPODIS'11 Proceedings of the 15th international conference on Principles of Distributed Systems
Secure Distributed Subgroup Discovery in Horizontally Partitioned Data

Transactions on Data Privacy
Secure k-NN computation on encrypted cloud data without sharing key with query users

Proceedings of the 2013 international workshop on Security in cloud computing
Secure k-NN query on encrypted cloud database without key-sharing

International Journal of Electronic Security and Digital Forensics

Quantified Score

Hi-index	0.00

Visualization

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.