Deep learning and computer vision have experienced significant advancements, particularly in critical applications such as autonomous driving and real-time surveillance, where object detection (OD) plays a pivotal role. Ensuring the accuracy and speed of these systems is paramount to prevent accidents or failures. Recently, latency-based attacks have emerged as a new threat, driven by the essential need for real-time performance in various applications. These attacks target model responsiveness to disrupt system performance without necessarily compromising accuracy. Our preliminary experiments show that introducing just a few bit flips to key parameters in OD models can significantly increase latency, degrading performance. Meanwhile, recent advancements in memory-based attacks, such as Row Hammer [18], demonstrate the ability to conveniently introduce bit flips at desired locations without physical hardware interaction. Based on the observations, we propose a novel attack on OD models that leverages row-hammer to introduce bit-flips via side channels, targeting the non-maximum suppression (NMS) filter and significantly increasing latency. Unlike previous methods that modify input data, our technique ensures efficiency by minimizing bit-flips through critical path exploitation and achieves practical applicability with only a subset of validation data. Experiments across various datasets and models validate our approach, demonstrating latency increases up to 71.6 ms (20.4×) with just 31 bit-flips.

Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. However, ensuring privacy within this quantum computing necessitates innovative approaches. Existing research has introduced the concept of quantum differential privacy (QDP) to protect data privacy in quantum computing by leveraging quantum noise. Yet, this method faces limitations due to the fixed and uncontrollable nature of the inherent noise, which directly affects the privacy budget of QDP. Addressing this critical gap, our study proposes a novel approach that utilizes quantum error correction (QEC) techniques not only to mitigate quantum computing errors but also to adjust QDP protection levels precisely. By selectively applying QEC to single or multiple qubit gates, we introduce a method to manipulate the quantum noise error rate effectively. Moreover, we derive a new formula for calculating the overall error rate in a quantum circuit and the adjusted privacy budget after QEC operation. Through extensive numerical simulations, we validate the efficacy of utilizing QEC in tuning privacy protection levels within quantum computing.

Quantum Neural Networks (QNNs) are one of the most promising applications that can be implemented on NISQ-era quantum computers. In this study, we observe that QNNs often suffer from gate redundancy, which hugely declines the performance and accuracy of the network. Even state-of-the-art architecture search techniques like QuantumNAS do not completely alleviate this problem. Especially, we find that CNOT gates are major contributors to the execution delay and noise in quantum circuits, and there are many redundant CNOT gates in the QNN post-training. This motivates us to propose a novel distribution-based greedy-search circuit optimization technique that can be employed after the completion of the training process. Our technique significantly reduces the number of CNOT gates in QNNs without affecting the accuracy of the network. With this technique, we have achieved an average of 3× improvement in execution time while reaching a maximum of 12.4× improvement.

Recent advancements in Convolution Neural Networks (CNNs) have achieved amazing success in numerous applications. The record-breaking performance of CNNs is usually at the prohibitive training costs, thus all training data are usually processed at the powerful centralized server side, which rises privacy concerns. Federated learning (FL) is a distributed machine learning method over mobile devices to train a global model while keeping decentralized data on devices to preserve the data privacy. However, there are two major limitations to deploy FL on mobile clients. Firstly, on the client side, the limited communication and computation resources on mobile devices cannot well support the full training iterations. Secondly, on the server side, conventional FL only aggregate a common output for all the clients without personalizing the model to each client, which is an important missing feature when clients have heterogeneous data distributions. In this work, we aim to enable low-cost personalized FL by focusing on the weight gradients which are the most important exchanging parameters in FL and meanwhile, dominating the computation and communication cost. We first observe that the client's calculated weight gradients have high sparsity, and the sparse pattern in weight gradients could be predicted via very simple bit-wise operations on a sequence of bits (named bit-stream) instead of conducting expensive high-precision calculations to determine them. Furthermore, a unique pattern is exhibited in each client's uploaded weight gradients according to the distribution of its local training data. Guided by this pattern, each client can get a personalized aggregated model to fit its own data. Hence, we leverage bit-streams to predict weight gradients sparsity for low-cost training on each device, and meanwhile, bit-streams are used to represent the unique sparse pattern of the weight gradient for each client which will guide the model personalization. From our experiments, our proposed framework can improve the computation efficiency by 3.5× on average (up to 4.2×) and reduce the communication cost by 23% on average (up to 41%) while still achieving the state-of-the-art personalized accuracy.