Research Article Open Access

Enhancing the Traffic Privacy with Leakage Detection and Optimization

Abstract

With recent experimental works, it is found that there is a information leakage in the packetized flow of data due to user activity and traffic content. We aim at understanding how complex when the information leaked by packet traffic features namely packet length, direction and times. Technique to call this type of feature is called traffic masking. Here, we define a security model to find the ideal target of masking which removes any leakage. Further, we investigate there is a tradeoff between traffic privacy protection and making cost, namely required amount of overhead and realization complexity feasibility. Major findings are that 1). Masking is a security model to find the ideal target of masking and it removes the leakage of packets in the network. Masking achieves similar overhead values with padding only and in case fragmentation is allowed and 2) Optimized statistical masking attains only moderately better overhead than simple fixed pattern masking does, while still leaking correlation information that can be exploited.

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