Provably Robust Decisions based on Potentially Malicious Sources of Information

Muller, Tim; Wang, Dongxia; Sun, Jun

Abstract

Sometimes a security-critical decision must be made using information provided by peers. Think of routing messages, user reports, sensor data, navigational information, blockchain updates. Attackers manifest as peers that strategically report fake information. Trust models use the provided information, and attempt to suggest the correct decision. A model that appears accurate by empirical evaluation of attacks may still be susceptible to manipulation. For a security-critical decision, it is important to take the entire attack space into account. Therefore, we define the property of robustness: the probability of deciding correctly, regardless of what information attackers provide. We introduce the notion of realisations of honesty, which allow us to bypass reasoning about specific feedback. We present two schemes that are optimally robust under the right assumptions. The “majority-rule” principle is a special case of the other scheme which is more general, named “most plausible realisations”.

• Conference Name: 2020 IEEE 33rd Computer Security Foundations Symposium (CSF)
• Conference Location: Boston, Massachusetts, USA.
• Start Date: Jun 22, 2020
• End Date: Jun 26, 2020
• Acceptance Date: Apr 20, 2020
• Online Publication Date: Aug 4, 2020
• Publication Date: Jun 22, 2020
• Deposit Date: Sep 18, 2023
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 411-425
• Book Title: 2020 IEEE 33rd Computer Security Foundations Symposium (CSF)
• ISBN: 9781728165738
• DOI: https://doi.org/10.1109/csf49147.2020.00036
• Public URL: https://nottingham-repository.worktribe.com/output/5227208
• Publisher URL: https://ieeexplore.ieee.org/document/9155184