Timing Channels in Adaptive Neural Networks
Ayomide Akinsanya
Network and Distributed System Security (NDSS) Symposium 2024 · Day 1 · Adversarial ML
Modern deep neural networks (DNNs) have revolutionized various fields with their impressive predictive capabilities, yet they often demand substantial computational resources. This challenge has spurred the development of **Adaptive Neural Networks (ADNNs)**, a class of optimized models designed to dynamically adjust their computational effort based on input characteristics. ADNNs aim to achieve faster predictions, particularly for "easy" inputs, by trading off some accuracy for enhanced efficiency. While their performance benefits have been extensively studied, the security ramifications of these input-dependent optimizations have remained largely unexplored. This seminal work by Ayomide Akinsanya systematically investigates and demonstrates how these efficiency-driven optimizations can inadvertently introduce critical **timing-channel vulnerabilities**.