A Self-Adaptive and Secure Approach to Share Network Trace Data

A large volume of network trace data are collected by the government, public, and private organizations, and can be analyzed for various purposes such as resolving network problems, improving network performance, and understanding user behavior. However, most organizations are reluctant to share their data with any external experts for analysis because it contains sensitive information deemed proprietary to the organization, thus raising privacy concerns. Even if the payload of network packets is not shared, header data may disclose sensitive information that adversaries can exploit to perform unauthorized actions. So network trace data needs to be anonymized before being shared. Most of existing anonymization tools have two major shortcomings: 1) they cannot provide provable protection; 2) their performance relies on setting the right parameter values such as the degree of privacy protection and the features that should be anonymized, but there is little assistance for a user to optimally set these parameters. This paper proposes a self-adaptive and secure approach to anonymize network trace data, and provides provable protection and automatic optimal settings of parameters. A comparison of the proposed approach with existing anonymization tools via experimentation demonstrated that the proposed method outperforms the existing anonymization techniques.