PROJECT TITLE :

Obfuscation of Sensitive Data for Incremental Release of Network Flows

ABSTRACT:

Giant datasets of real network flows acquired from the Internet are an invaluable resource for the research community. Applications embrace network modeling and simulation, identification of security attacks, and validation of analysis results. Unfortunately, network flows carry extremely sensitive info, and this discourages the publication of these datasets. Indeed, existing techniques for network flow sanitization are prone to completely different types of attacks, and solutions proposed for microdata anonymity can not be directly applied to network traces. In our previous analysis, we proposed an obfuscation technique for network flows, providing formal confidentiality guarantees beneath realistic assumptions about the adversary's data. During this paper, we establish the threats posed by the incremental unleash of network flows, we have a tendency to propose a unique defense algorithm, and we tend to formally prove the achieved confidentiality guarantees. An extensive experimental analysis of the algorithm for incremental obfuscation, distributed with billions of real Internet flows, shows that our obfuscation technique preserves the utility of flows for network traffic analysis.