SC2D: an alternative to trace anonymization

Progress in networking research depends crucially on applying novel analysis tools to real-world traces of network activity. This often conflicts with privacy and security requirements; many raw network traces include information that should never be revealed to others.The traditional resolution of this dilemma uses trace anonymization to remove secret information from traces, theoretically leaving enough information for research purposes while protecting privacy and security. However, trace anonymization can have both technical and non-technical drawbacks.We propose an alternative to trace-to-trace transformation that operates at a different level of abstraction. Since the ultimate goal is to transform raw traces into research results, we say: cut out the middle step. We propose a model for shipping flexible analysis code to the data, rather than vice versa. Our model aims to support independent, expert, prior review of analysis code. We propose a system design using layered abstraction to provide both ease of use, and ease of verification of privacy and security properties. The system would provide pre-approved modules for common analysis functions. We hope our approach could significantly increase the willingness of trace owners to share their data with researchers. We have loosely prototyped this approach in previously published research.