Encrypted tunnels are increasingly applied to privacy protection, however, a passive eavesdropper can still infer which website a user is visiting via website fingerprinting (WF). State-of-the-art WF suffers from several critical challenges in a realistic multi-tab scenario, where the number of concurrent tabs is dynamic and uncertain, training a separate model for each website is too overweight to deploy, and the robustness against the packet loss, duplication and disorder caused by dynamic network conditions is rarely considered. In this paper, we propose a robust lightweight multi-tab WF method, named RobustWF. Due to the causality relationship between user's request and website's response, RobustWF employs causality correlation to associate the interactive packets belonging to the same website together, which form a causality chain. Then, RobustWF utilizes context learning to capture the dependencies between the causality chains. The missing of some specific details does not have a significant impact on the overall structure of target web, thus enhancing the robustness of RobustWF. To make the model lightweight enough, RobustWF trains an integrated model to adapt to the dynamic number of concurrent tabs. The experimental results demonstrate that the accuracy of RobustWF improves 14% in dynamic multi-tab WF scenario compared to the State-of-the-art method.

Although Payment Channel Network (PCN) has been proposed as a second-layer solution to the scalability issue of blockchain-based cryptocurrencies, most developed systems still struggle to handle the ever-increasing usage. Virtual payment channel (VPC) has been proposed as an off-chain technique that avoids the involvement of intermediaries for payments in a PCN. However, there is no research on how to efficiently construct VPCs while considering the characteristics of the underlying PCN. To fill this void, this paper focuses on the VPC construction in a PCN. More specifically, we propose a metric, Capacity to the Number of Intermediaries Ratio (CNIR), to consider both the capacity of the constructed VPC and the collateral locked by the involved users. We first study the VPC construction problem for a single pair of users and design an efficient algorithm that achieves the optimal CNIR. Based on this, we propose Thor, a protocol that constructs a virtual payment channel network for multiple pairs. Evaluation results show that Thor can construct VPCs with maximum CNIR in single-pair cases and efficiently construct VPCs with high CNIRs for multi-pair cases, compared to baseline algorithms.

Transport Layer Security (TLS) connections involve costly cryptographic operations which incur significant resource consumption in the cloud. Hardware accelerators are affordable substitutes of expensive CPU cores to accommodate with the constantly increasing security requirements of datacenters. Existing accelerators virtualization mainly relies on passthrough of Single Root I/O Virtualization (SR-IOV) devices. However, deficiency of service accessibility, functionality and availability make device passthrough not an optimal solution for heterogeneous accelerators with different capabilities. To make up the gap, we propose vCrypto, a unified para-virtualization framework for heterogeneous cryptographic resources. vCrypto supports stateful crypto requests offloading and result retrieval with session lifecycle management and event driven notification. vCrypto transparently integrates virtual crypto device capabilities into the OpenSSL framework to benefit existing applications that are based on crypto library APIs without modification. Multiple physical resources can be partitioned flexibly and scheduled cooperatively to enhance the functionality, performance and robustness of virtual crypto service. Finally, vCrypto achieves an optimized performance with two layers polling and memory sharing mechanism. The comprehensive experiments show that with the same cryptographic resources used, vCrypto framework can provide 2.59x to 3.36x higher AES-CBC-HMAC-SHA1 throughput compared to passthrough SR-IOV device.

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing their private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt mode updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE does not consider the heterogeneity among different clients, an intrinsic problem in cross-silo FL, often resulting in undermined training efficiency with slow convergence and stragglers. In this work, we propose FedPHE, an efficiently packed homomorphically encrypted FL framework with secure weighted aggregation and client selection to tackle the heterogeneity problem. Specifically, using CKKS with sparsification, FedPHE can achieve efficient encrypted weighted aggregation by accounting for contributions of local updates to the global model. To mitigate the straggler effect, we devise a sketching-based client selection scheme to cherry-pick representative clients with heterogeneous models and computing capabilities. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve a training speedup of 1.85-4.44\times, cut the communication overhead by 1.24-22.62\times, and reduce the straggler effect by up to 1.71-2.39\times.

Precise vehicle positioning is the fundamental technology in vehicle platooning, for sensing the driving environment, detecting hazards, and determining driving strategies. While radars offer robust performance in extreme weather, the positioning error of up to 1.8 m could be insufficient for platoons with vehicle spacings of a few meters. In contrast, LiDARs provide great accuracy, but, its high cost hinders its penetration rate in the commercial market. To this end, this paper presents MatrixLoc, utilizing a projector as the headlight and a customized light sensor array as the receiver to achieve low-cost and centimeter-level relative vehicle positioning in both longitudinal and lateral axes, along with bearing information. MatrixLoc leverages differential phase shift keying (DPSK) modulation to create a fringe pattern, enabling single-shot positioning. Accurate positioning is achieved by analyzing the observed space-varying frequency and demodulated phase information. Real-world driving results demonstrate that MatrixLoc achieves centimeter-level positioning accuracy, with a positioning error of 30 cm and a bearing error of 9 degrees at a distance of 20 m.

Camera sensors have been widely used to perceive the vehicle surrounding environments, understand the traffic condition, and then help avoid traffic accidents. Since most sensors are limited by line of sight, the perception data collected through individual vehicle can be uploaded and shared through the edge server. To reduce the bandwidth, storage and processing cost, we propose an edge-assisted camera selection system that only selects the necessary camera images to upload to the server. The selection is based on the camera metadata which describes the coverage of the cameras represented with GPS locations, orientations, and field of views. Different from existing work, our metadata based approach can detect and locate camera occlusions by leveraging LiDAR sensors, and then precisely and quickly calculate the real camera coverage and identify the coverage overlap. Based on the camera metadata, we study two camera selection problems, the Max-Coverage problem and the Min-Selection problem, and solve them with efficient algorithms. Moreover, we propose similarity based redundancy suppression techniques to further reduce the bandwidth consumption which becomes significant due to vehicle movements. Extensive evaluations demonstrate that the proposed algorithms can effectively select cameras to maximize coverage or minimize bandwidth consumption based on the application requirements.

This paper presents COPILOT, a ML-based approach that allows vehicles requiring ubiquitous high bandwidth connectivity to identify the most suitable road side units (RSUs) through proactive handoffs. By cooperatively exchanging the data obtained from local 3D Lidar point clouds within adjacent vehicles and with coarse knowledge of their relative positions, COPILOT identifies transient blockages to all candidate RSUs along the path under study. Such cooperative perception is critical for choosing RSUs with highly directional links required for mmWave bands, which majorly degrade in the absence of LOS. COPILOT proposes three modules that operate in an inter-connected manner: (i) As an alternative to sending raw Lidar point clouds, it extracts and transmits low-dimensional intermediate features to lower the overhead of inter-vehicle messaging; (ii) It utilizes an attention-mechanism to place greater emphasis on data collected from specific cars, as opposed to naive nearest neighbor and distance-based selection schemes, and (iii) it experimentally validates the outcomes using an outdoor testbed composed of an autonomous car and Talon AD7200 60GHz routers emulating the RSUs, accompanied by the public release of the datasets. Results reveal COPILOT yields upto 69.8% and 20.42% improvement in latency and throughput compared to traditional reactive handoffs for mmWave networks, respectively.

Point cloud registration (PCR) can significantly extend the visual field and enhance the point density on distant objects, thereby improving driving safety. However, it is very challenging for vehicles to perform online registration between long-range point clouds. In this paper, we propose an online long-range PCR scheme in VANETs, called LoRaPCR, where vehicles achieve long-range registration through multi-hop short-range highly-accurate registrations. Given the NP-hardness of the problem, a heuristic algorithm is developed to determine best registration paths while leveraging the reuse of registration results to reduce computation cost. Moreover, we utilize an optimized dynamic programming algorithm to determine the transmission routes while minimizing the communication overhead. Results of extensive simulations demonstrate that LoRaPCR can achieve high PCR accuracy with low relative translation and rotation errors of 0.55 meters and 1.43°, respectively, at a distance of over 100 meters, and reduce the computation overhead by more than 50% compared to the state-of-the-art method.