Privacy-Safe Network Trace Sharing via Secure Queries
Contemporary network researches are difficult, because
Users don't want their information to be leaked, therefore
ISPs try to avoid such thing from happening by sanitizing
traces or not publishing at all.
Researchers, on the other hand, need rich data for their
Researchers also will benefit from a more powerful tool that
is easy to use with more designated features for trace
Our solution: Privacy-safe network trace sharing
framework, with privacy and security in mind via Trol and Patrol.
Patrol is designed to solve the problems mentioned above. It
provides a framework that securely stores traces and provide a
query interface for users, and return results that are
fine-tuned to avoid leaking sensitive data, while still with
enough statistical information for research purposes. Queries are
expressed in an SQL-like language, Trol, that supports most common
trace processing primitives.
A trace analysis tool: support common data query functions
including grouping, keeping, foreach,
conditional/relational/arithmetic operations, output in
histogram, CDF, etc.
A privacy enforced tool: protect data privacy using enhanced
K-Anonymity, with protection against tracker and faker.
Trol/Patrol are in active development, and our source code will be
publicly available soon.
- Commoner Privacy And A Study On Network Traces, Xiyue Deng and Jelena Mirkovic, Proceedings of 2017 Annual Computer Security Applications Conference (ACSAC), 2017.
This research is supported by the NSF CNS award number 0914780.