Private Representative-Based Clustering for Vertically Partitioned Data

Authors:
Vladimir Estivill-Castro
Affiliations:
Griffith University
Venue:
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
Year:
2004

Citing 0
Cited 4

The privacy of k-NN retrieval for horizontal partitioned data: new methods and applications

ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
A new efficient privacy-preserving scalar product protocol

AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
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
Privacy preserving DBSCAN for vertically partitioned data

ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper studies how to construct a representative-based clustering algorithms under the scenario that the dataset is partitioned into at least two sections. One section of the data is owned by Alice while the other is owned by Bob. Both want to compute clusters from the union of the data but do not trust each other. Thus, they do not want the other party to learn anything about their share of the data except what can be inferred from the results. We present a protocol that allows Alice and Bob to carry this task under the k-medoids algorithm. Clustering with medoids (medians or other loss functions) is a more robust alternative that clustering with k-MEANS (the only method for which a privacy preserving protocol is known, but a methods that is statistically biased and statistically inconsistent with very low robustness to noise). Our approach highlights the necessary building blocks for extending our protocol to the family of representative-based clustering algorithms.