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|Scopus||Web of Science®|
|Title:||Multi-observer privacy-preserving hidden Markov models|
|Citation:||Proceedings of the 2012 IEEE Network Operations and Management Symposium, held in Maui, Hawaii, USA, 16-20 April, 2012 / F. De Turck, L.P. Gaspary and D. Medhi (eds.): pp.514-517|
|Series/Report no.:||IEEE IFIP Network Operations and Management Symposium|
|Conference Name:||IEEE Network Operations and Management Symposium (2012 : Maui, Hawaii)|
|Hung X. Nguyen and Matthew Roughan|
|Abstract:||Detection of malicious traffic and network health problems would be much easier if ISPs shared their data. Unfortunately, they are reluctant to share because doing so would either violate privacy legislation or expose business secrets. However, secure distributed computation allows calculations to be made using private data, without leaking this data. This paper presents such a method, allowing multiple parties to jointly infer a Hidden Markov Model (HMM) for traffic and/or user behaviour in order 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 implement a prototype of the protocols, and our experiments with the prototype show its has a reasonable computational and communications overhead, making it practical for adoption by ISPs.|
|Rights:||© 2012 IEEE|
|Appears in Collections:||Mathematical Sciences publications|
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