Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks

This paper aims to provide differentiated security protection for infotainment data communication in Internet-of-Vehicle (IoV) networks. The IoV is a network of vehicles that uses various sensors, software, built-in hardware, and communication technologies to enable information exchange between pedestrians, cars, and urban infrastructure. Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks. The attacker can spread false information about traffic conditions, mislead drivers in their directions, and interfere with traffic management. Such attacks can also cause distractions to the driver, which has a potential implication for the safety of driving. The existing literature on IoV communication and network security focuses mainly on generic solutions. In a heterogeneous communication network where different types of communication coexist, we can improve the efficiency of security solutions by considering the different security and efficiency requirements of data communications. Hence, we propose a differentiated security mechanism for protecting infotainment data communication in IoV networks. In particular, we first classify data communication in the IoV network, examine the security focus of each data communication, and then develop a differentiated security architecture to provide security protection on a file-to-file basis. Our architecture leverages Named Data Networking (NDN) so that infotainment files can be efficiently circulated throughout the network where any node can own a copy of the file, thus improving the hit ratio for user file requests. In addition, we propose a time-sensitive Key-Policy Attribute-Based Encryption (KP-ABE) scheme for sharing subscription-based infotainment data...Comment: 16th International Conference on Network and System Securit

Durable superhydrophobic polyvinylidene fluoride membranes via facile spray-coating for effective membrane distillation

Membrane wetting and fouling substantially limits application and deployment of membrane distillation process. Designing high-performance superhydrophobic membranes offers an effective solution to solve the challenge. In this work, a highly durable superhydrophobic surface (water contact angle of 170.8 ± 1.3°) was constructed via a facile and rapid spray-coating of extremely hydrophobic SiO2 nanoparticles onto a porous polyvinylidene fluoride (PVDF) substrate for membrane distillation. The superhydrophobic membrane coated by fluorinated SiO2 nanoparticles exhibited a superior physicochemical stability in a wide range of extreme environments (i.e., NaOH, HCl, hot water, rust water, humic acid solution, ultrasonication, and high-speed water scouring). During 8-h continuous membrane distillation desalination experiment, the coated superhydrophobic membrane experienced a consistently stable water vapor flux (ca. 19.1 kg·m−2·h−1) and desalination efficiency (99.99 %). Additionally, such a stable superhydrophobicity endowed the spray-coated PVDF membrane to overcome membrane wetting and fouling during membrane distillation of highly saline solutions containing foulants (i.e., humic acid and rust). Results reported in this study provides a useful concept and strategy in facile construction of robust superhydrophobic membranes via spray-coating for effective membrane distillation.</p

Privacy-Preserving Aggregation in Federated Learning: A Survey

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry. Practical PPFL typically allows multiple participants to individually train their machine learning models, which are then aggregated to construct a global model in a privacy-preserving manner. As such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has received substantial research interest. This survey aims to fill the gap between a large number of studies on PPFL, where PPAgg is adopted to provide a privacy guarantee, and the lack of a comprehensive survey on the PPAgg protocols applied in FL systems. In this survey, we review the PPAgg protocols proposed to address privacy and security issues in FL systems. The focus is placed on the construction of PPAgg protocols with an extensive analysis of the advantages and disadvantages of these selected PPAgg protocols and solutions. Additionally, we discuss the open-source FL frameworks that support PPAgg. Finally, we highlight important challenges and future research directions for applying PPAgg to FL systems and the combination of PPAgg with other technologies for further security improvement.Comment: 20 pages, 10 figure

Flavonoids from Lycium barbarum leaves attenuate obesity through modulating glycolipid levels, oxidative stress, and gut bacterial composition in high-fat diet-fed mice

Traditional herbal therapy made from Lycium barbarum leaves has been said to be effective in treating metabolic diseases, while its exact processes are yet unknown. Natural flavonoids are considered as a secure and reliable method for treating obesity. We thus made an effort to investigate the processes by which flavonoids from L. barbarum leaves (LBLF) reduce obesity. To assess the effectiveness of the intervention following intragastric injection of various dosages of LBLF (50, 100, and 200 mg/kg⋅bw), obese model mice developed via a high-fat diet were utilized. Treatment for LBLF may decrease body weight gain, Lee’s index, serum lipids levels, oxidative stress levels, and hepatic lipids levels. It may also enhance fecal lipids excretion and improve glucose tolerance. Additionally, LBLF therapy significantly restored gut dysfunction brought on by a high-fat diet by boosting gut bacterial diversities and altering the composition of the gut bacterial community by elevating probiotics and reducing harmful bacteria

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.Comment: 7 pages, 3 figures in the main text, and 13 pages, 13 figures, and 1 table in supplementary material

Experimental quantum computational chemistry with optimised unitary coupled cluster ansatz

Simulation of quantum chemistry is one of the most promising applications of quantum computing. While recent experimental works have demonstrated the potential of solving electronic structures with variational quantum eigensolver (VQE), the implementations are either restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors for the more accurate unitary coupled cluster (UCC) ansatz. Here, integrating experimental and theoretical advancements of improved operations and dedicated algorithm optimisations, we demonstrate an implementation of VQE with UCC for H_2, LiH, F_2 from 4 to 12 qubits. Combining error mitigation, we produce high-precision results of the ground-state energy with error suppression by around two orders of magnitude. For the first time, we achieve chemical accuracy for H_2 at all bond distances and LiH at small bond distances in the experiment. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.Comment: 8 pages, 4 figures in the main text, and 29 pages supplementary materials with 16 figure

Cellular and Molecular Mechanisms Underlying Synaptic Subcellular Specificity

Chemical synapses are essential for neuronal information storage and relay. The synaptic signal received or sent from spatially distinct subcellular compartments often generates different outcomes due to the distance or physical property difference. Therefore, the final output of postsynaptic neurons is determined not only by the type and intensity of synaptic inputs but also by the synaptic subcellular location. How synaptic subcellular specificity is determined has long been the focus of study in the neurodevelopment field. Genetic studies from invertebrates such as Caenorhabditis elegans (C. elegans) have uncovered important molecular and cellular mechanisms required for subcellular specificity. Interestingly, similar molecular mechanisms were found in the mammalian cerebellum, hippocampus, and cerebral cortex. This review summarizes the comprehensive advances in the cellular and molecular mechanisms underlying synaptic subcellular specificity, focusing on studies from C. elegans and rodents