Multi-Observer Privacy-Preserving Hidden Markov Models
Source: University of Adelaide
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 behavior in order to detect anomalies. The authors 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.