Defensive Design of Saturating Counters Based on Differential Privacy
Depeng Liu, Lutan Zhao, Pengfei Yang, Bow-Yaw Wang, Rui Hou, Lijun, Zhang, Naijun Zhan

TL;DR
This paper introduces a differential privacy-based approach to design saturating counters in branch prediction, enhancing security against inference attacks while maintaining performance.
Contribution
It proposes a novel probabilistic saturating counter design that guarantees differential privacy, preventing attackers from inferring sensitive information from counter states.
Findings
Theoretical analysis confirms the security guarantees of the new counters.
Experimental results show comparable performance to existing counters.
The new counters effectively prevent branch inference attacks.
Abstract
The saturating counter is the basic module of the dynamic branch predictor, which involves the core technique to improve instruction level parallelism performance in modern processors. However, most studies focus on the performance improvement and hardware consumption of saturating counters, while ignoring the security problems they may cause. In this paper, we creatively propose to study and design saturating counters from the defense perspective of differential privacy, so that attackers cannot distinguish the states that saturating counters are in and further infer sensitive information. To obtain theoretical guarantees, we use Markov chain to formalize the attack algorithm applied to the saturating counter, investigate into the optimal attack strategy and calculate the probability of successful attack. Furthermore, we find that the attacker is able to accurately guess the branch…
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Taxonomy
TopicsSecurity and Verification in Computing · Cloud Data Security Solutions · Network Security and Intrusion Detection
