Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/86996
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Type: Journal article
Title: Multi-observer privacy-preserving hidden Markov models
Author: Nguyen, H.
Roughan, M.
Citation: IEEE Transactions on Signal Processing, 2013; 61(23):6010-6019
Publisher: IEEE
Issue Date: 2013
ISSN: 1053-587X
1941-0476
Statement of
Responsibility: 
Hung X. Nguyen, and Matthew Roughan
Abstract: Detection of malicious traffic and network health problems would be much easier if Internet Service Providers (ISPs) shared their data. Unfortunately, they are reluctant to share because doing so would either violate privacy legislation or expose business secrets. Secure distributed computation allows calculations to be made using private data and provides an ideal mechanism for ISPs to share their data. This paper presents such a method, allowing multiple parties to jointly infer a Hidden Markov Model (HMM) for network traffic, which can then be used to detect anomalies. We extend prior work on HMMs in network security to include observations from multiple ISPs and develop secure protocols to infer the model parameters without revealing the private data. We implemented a prototype of the protocols and have tested our implementation on simulated data of realistic network attack models. The experiments show that our protocols have small computation and communication overheads. The protocols therefore are suitable for adoption by ISPs.
Keywords: Hidden Markov model; multi-observer; network security; privacy preserving
Rights: © 2013 IEEE
RMID: 0030007744
DOI: 10.1109/TSP.2013.2282911
Appears in Collections:Mathematical Sciences publications

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