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This paper presents a schedule randomization algorithm that reduces thevulnerability of real-time systems to timing inference attacks which attempt tolearn the timing of task execution. It utilizes run-time information readilyavailable at each scheduling decision point to increase the level ofuncertainty in task schedules, while preserving the original schedulability.The randomization algorithm significantly reduces an adversary's best chance tocorrectly predict what tasks would run at arbitrary times. This paper alsoproposes an information-theoretic measure that can quantify the worst-casevulnerability, from the defender's perspective, of an arbitrary real-timeschedule.

TaskShuffler++: Real-Time Schedule Randomization for Reducing Worst-Case Vulnerability to Timing Inference Attacks