In the Noisy Intermediate Scale Quantum (NISQ) era, the dynamic nature of quantum systems causes noise sources to constantly vary over time. Transient errors from the dynamic NISQ noise landscape are challenging to comprehend and are especially detrimental to classes of applications that are iterative and/or long-running, and therefore their timely mitigation is important for quantum advantage in real-world applications.

Distributed Multi-exit Neural Networks (MeNNs) use partitioning and early exits to reduce the cost of neural network inference on low-power sensing systems. Existing MeNNs exhibit high inference accuracy using policies that select when to exit based on data-dependent prediction confidence. This paper presents a side-channel attack against distributed MeNNs employing data-dependent early exit policies. We find that an adversary can observe when a distributed MeNN exits early using encrypted communication patterns. An adversary can then use these observations to discover the MeNN's predictions with over 1.85× the accuracy of random guessing. In some cases, the side-channel leaks over 80% of the model's predictions. This leakage occurs because prior policies make decisions using a single threshold on varying prediction confidence distributions. We address this problem through two new exit policies. The first method, Per-Class Exiting (PCE), uses multiple thresholds to balance exit rates across predicted classes. This policy retains high accuracy and lowers prediction leakage, but we prove it has no privacy guarantees. We obtain these guarantees with a second policy, Confidence-Guided Randomness (CGR), which randomly selects when to exit using probabilities biased toward PCE's decisions. CGR provides statistically equivalent privacy with consistently higher inference accuracy than exiting early uniformly at random. Both PCE and CGR have low overhead, making them viable security solutions in resource-constrained settings.

Adaptive sampling is a powerful family of algorithms for managing energy consumption on low-power sensors. These algorithms use captured measurements to control the sensor's collection rate, leading to near-optimal error under energy constraints. Adaptive sampling's data-driven nature, however, comes at a cost in privacy. In this work, we demonstrate how the collection rates of general adaptive policies leak information about captured measurements. Further, individual adaptive policies display this leakage on multiple tasks. This result presents a challenge in maintaining privacy for sensors using energy-efficient batched communication. In this context, the size of measurement batches exposes the sampling policy's collection rate. Thus, an attacker who monitors the encrypted link between sensor and server can use message lengths to uncover information about the captured values. We address this side-channel by introducing a framework called Adaptive Group Encoding (AGE) that protects any periodic adaptive sampler. AGE uses quantization to encode all batches as fixed-length messages, making message sizes independent of the collection rate. AGE reduces the quantization error through a series of transformations. The proposed framework preserves the low error of adaptive sampling while preventing information leakage and incurring negligible energy overhead.

Recurrent neural networks (RNNs) are well-suited to the sequential inference tasks often found in embedded sensing systems. While RNNs have displayed high accuracy on many tasks, they are poorly equipped for inference under energy budgets that are unknown at design time. Existing RNNs meet energy constraints in sensor environments by training models to subsample input sequences. The tight coupling between the sampling strategy and the RNN prevents these systems from generalizing to new energy budgets at runtime. To address this problem, we present a novel RNN architecture called the Budget RNN. Budget RNNs use a leveled architecture to decouple the sampling strategy from the RNN model, allowing a single Budget RNN to change its subsampling behavior at runtime. We further propose a runtime feedback controller to optimize the model's accuracy for a given energy budget. Across a set of budgets, the Budget RNN inference system achieves a mean accuracy of roughly 3 points higher than standard RNNs. Alternatively, Budget RNNs can achieve comparable accuracy to existing RNNs while under 20% smaller budgets.