Modern vehicles increasingly rely on advanced driver monitoring systems (DMS) to ensure safety and enhance the driving experience. These systems assess driver status to prevent accidents caused by fatigue, inattentiveness, or intoxication. While some DMS applications process video data on vehicle, many rely on edge or cloud-based solutions, raising significant privacy concerns due to the storage of sensor data from vehicles. Existing approaches, such as de-identification and homomorphic encryption, either impose heavy computational overhead on vehicles or insufficiently address privacy. To overcome these limitations, we present the Privacy-preserving Driver Monitoring System (PDMS), a novel framework based on the additive secret sharing theory and privacy-preserving Transformer-based deep learning models. PDMS creates randomized secret shares from driver’s facial video data on vehicle, processes them independently through privacy-preserving Transformer models on edges, and securely aggregates partial results on vehicle, ensuring vehicles’ sensor data and final results remain protected. This approach reduces the computational load on the vehicle, enabling cost-effective and scalable DMS solutions that protect the privacy of the driver both in transit and in processing. Our contributions include the design and optimization of the PDMS system, incorporating privacy-preserving DNN layers that are capable of processing randomized secret shares. Furthermore, we present a practical system that utilizes a vision transformer (ViT)-based gaze estimation model, demonstrating the effectiveness of PDMS through comprehensive experiments.

This paper introduces a novel framework, M4, designed to estimate per-flow quantiles in data streams accurately. M4 is a versatile framework that can be integrated with a wide array of single-flow quantile estimation algorithms, thereby enabling them to perform per-flow estimation. The framework employs a sketch-based approach to provide a space-efficient method for recording and extracting distribution information. M4 incorporates two techniques:MINIMUMandSUM. TheMINIMUMtechnique minimizes the noise on a flow from other flows caused by hash collisions, while theSUMtechnique efficiently categorizes flows based on their sizes and customizes treatment strategies accordingly. We demonstrate the application of M4 on three single-flow quantile estimation algorithms (DDSketch,$t$-digest, and ReqSketch), detailing the specific implementation of theMINIMUMandSUMtechniques. We provide theoretical proof that M4 delivers high accuracy while utilizing limited memory. Additionally, we conduct extensive experiments to evaluate the performance of M4 regarding accuracy and speed. The experimental results indicate that across all three example algorithms, M4 significantly outperforms two comparison frameworks in terms of accuracy for per-flow quantile estimation while maintaining comparable speed.

The field of quantile estimation has grown in importance due to its myriad practical applications. Recent research trends have evolved from estimating the quantile for a single data stream to developing data structures that can concurrently estimate quantiles for multiple sub-streams, also known as flows. This paper introduces a novel framework, M4, designed to estimate per-flow quantiles in data streams accurately. M4 is a versatile framework that can be integrated with a wide array of single-flow quantile estimation algorithms, thereby enabling them to perform per-flow estimation. The framework employs a sketch-based approach to provide a space-efficient method for recording and extracting distribution information. M4 incorporates two techniques: MINIMUM and SUM. The MINIMUM technique minimizes the noise on a flow from other flows caused by hash collisions, while the SUM technique efficiently categorizes flows based on their sizes and customizes treatment strategies accordingly. We demonstrate the application of M4 on three single-flow quantile estimation algorithms (DDSketch, t-digest, and ReqSketch), detailing the specific implementation of the MINIMUM and SUM techniques. We provide theoretical proof that M4 delivers high accuracy while utilizing limited memory. Additionally, we conduct extensive experiments to evaluate the performance of M4 regarding accuracy and speed. The experimental results indicate that across all three example algorithms, M4 significantly outperforms two comparison frameworks in terms of accuracy for per-flow quantile estimation while maintaining comparable speed.

Cooperative perception for connected and automated vehicles is traditionally achieved through the fusion of feature maps from two or more vehicles. However, the absence of feature maps shared from other vehicles can lead to a significant decline in 3D object detection performance for cooperative perception models compared to standalone 3D detection models. This drawback impedes the adoption of cooperative perception as vehicle resources are often insufficient to concurrently employ two perception models. To tackle this issue, we present Simultaneous Individual and Cooperative Perception (SiCP), a generic framework that supports a wide range of the state-of-the-art standalone perception backbones and enhances them with a novel Dual-Perception Network (DP-Net) designed to facilitate both individual and cooperative perception. In addition to its lightweight nature with only 0.13M parameters, DP-Net is robust and retains crucial gradient information during feature map fusion. As demonstrated in a comprehensive evaluation on the V2V4Real and OPV2V datasets, thanks to DP-Net, SiCP surpasses state-of-the-art cooperative perception solutions while preserving the performance of standalone perception solutions. The source code can be found at https://github.com/DarrenQu/SiCP.

With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as perception using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge units. However, data privacy becomes a major concern for vehicular edge computing, as sensor data with sensitive information from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles' sensor data and the detection results. In this paper, we present a privacy preserving perception$(\mathbf{P}^{3})$framework which provides a secure version of every commonly used layers in various perception CNN networks. They server as the building blocks to facilitate the construction of a privacy preserving CNN for any existing or future network.$\mathbf{P}^{3}$leverages the additive secret sharing theory to develop secure functions for perception networks. A vehicle's sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We present two use cases where the secure layers in$\mathbf{P}^{3}$are used to build privacy preserving both single-stage and two-stage object detection CNNs. Experimental results indicate data privacy for vehicles is protected without comprising the detection accuracy and with a reasonable amount of performance degradation. To the best of our knowledge, this is the first work that provides a generic framework to ease the development of vehicle-edge perception networks protecting data privacy.

In this paper, we present PRECISE, a novel privacy preserving data sharing framework for connected autonomous vehicles (CAVs). PRECISE allows users to define the objects or parts that they wish to protect privacy before sharing data with other vehicles. It leverages secure segmentation and inpainting technologies to protect sensitive data of vehicles. PRECISE explores the edges to offload resource-intensive deep learning workloads. To ensure data privacy in the processing on edge, PRECISE leverages additive secret sharing theory to define secure functions for deep neural networks (DNNs). Two secure DNN models, Secure SegNet and Secure Context Encoder, are introduced, along with detailed explanations of how to develop secure CNN layers and the secure functions used in building these layers. We have implemented a prototype of PRECISE and evaluated its performance. The experimental results demonstrate that PRECISE is lightweight, achieving secure segmentation in 3.47 seconds and secure inpainting in 0.99 seconds. The inference outputs from PRECISE remain the same as those from the original DNNs, while data privacy is protected. To the best of our knowledge, PRECISE is the first of its kind to provide user-defined privacy protection for sensor data sharing among CAVs.

Stochastic information about the reliability parameters of a test unit can be rapidly obtained via accelerated life tests by running the tests at higher stress levels than normal operating conditions. Using a regression model, the reliability parameter at the normal design stress can be estimated via extrapolation. Recently, the design optimization of accelerated life tests has been investigated by many researchers but the associated inference for the regression parameters has not been. In this article, the Expectation-maximization algorithm is used to determine the maximum likelihood estimates of the regression parameters for time constrained exponential failure data from the step-stress and constant-stress accelerated life tests with interval monitoring. It is demonstrated that the method is feasible as well as easy to implement. Using the principle of missing information, the asymptotic variances and covariances of the maximum likelihood estimates are also calculated. The proposed method is illustrated using a real engineering case study.

Lattice based cryptography is attractive for its quantum computing resistance and efficient encryption/decryption process. However, the big data problem has perplexed lattice based cryptographic systems with the slow processing speed. This paper intends to analyze one of the major lattice-based cryptographic systems, Nth-degree truncated polynomial ring (NTRU), and accelerate its execution with Graphic Processing Unit (GPU) for acceptable processing performance. Three strategies, including single GPU with zero copy, single GPU with data transfer, and multi-GPU versions are proposed. GPU computing techniques such as stream and zero copy are applied to overlap the computation and communication for possible speedup. Experimental results have demonstrated the effectiveness of GPU acceleration of NTRU. As the number of involved devices increases, better NTRU performance will be achieved.