Secure Distributed-Learning on Threat Intelligence

Cyber security information is often extremely sensitive and confidential, it introduces a tradeoff between the benefits of improved threat-response capabilities and the drawbacks of disclosing national-security-related information to foreign agencies or institutions. This results in the retention of valuable information (a.k.a. as the free-rider problem), which considerably limits the efficacy of data sharing. The purpose of this project is to resolve the cybersecurity information-sharing tradeoff by enabling more accurate insights on larger amounts of more relevant collective threat-intelligence data.
This project will have the benefit of enabling institutions to build better models by securely collaborating with valuable sensitive data that is not normally shared. This will expand the range of available intelligence, thus leading to new and better threat analyses and predictions.