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Bandwidth-Efficient Two-Server ORAMs with O(1) Client Storage

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arXiv 2025年

作者： Wang, Wei Zhang, Xianglong Xu, Peng Chen, Rongmao Yang, Laurence Tianruo School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Jinyinhu Laboratory Wuhan430040 China School of Computer National University of Defense Technology Changsha410073 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Department of Computer Science St. Francis Xavier University AntigonishNSB2G 2W5 Canada

Oblivious RAM (ORAM) allows a client to securely retrieve elements from outsourced servers without leakage about the accessed elements or their virtual addresses. Two-server ORAM, designed for secure two-party RAM computation, stores data across two non-colluding servers. However, many two-server ORAM schemes suffer from excessive local storage or high bandwidth costs. To serve lightweight clients, it is crucial for ORAM to achieve concretely efficient bandwidth while maintaining O(1) local storage. Hence, this paper presents two new client-friendly two-server ORAM schemes that achieve practical logarithmic bandwidth under O(1) local storage, while incurring linear symmetric key computations. The core design features a hierarchical structure and a pairwise-area setting for the elements and their tags. Accordingly, we specify efficient read-only and write-only private information retrieval (PIR) algorithms in our schemes to ensure obliviousness in accessing two areas respectively, so as to avoid the necessity of costly shuffle techniques in previous works. We empirically evaluate our schemes against LO13 (TCC’13), AFN17 (PKC’17), and KM19 (PKC’19) in terms of both bandwidth and time cost. The results demonstrate that our schemes reduce bandwidth by approximately 2 ∼ 4× compared to LO13, and by 16 ∼ 64× compared to AFN17 and KM19. For a database of size 214 blocks, our schemes are over 64× faster than KM19, while achieving similar performance to LO13 and AFN17 in the WAN setting, with a latency of around 1 second. Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual addresses

Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

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arXiv 2023年

作者： Liu, Xiaogeng Li, Minghui Wang, Haoyu Hu, Shengshan Ye, Dengpan Jin, Hai Wu, Libing Xiao, Chaowei School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China School of Cyber Science and Engineering Wuhan University China Arizona State University United States

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo)1, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime

Downstream-agnostic Adversarial Examples

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arXiv 2023年

作者： Zhou, Ziqi Hu, Shengshan Zhao, Ruizhi Wang, Qian Zhang, Leo Yu Hou, Junhui Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China School of Information and Communication Technology Griffith University Australia Department of Computer Science City University of Hong Kong Hong Kong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial use. In this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://github. com/CGCL-codes/AdvEncoder. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach

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arXiv 2024年

作者： Du, Hongyang Liu, Guangyuan Lin, Yijing Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLM-enabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems. © 2024, CC BY.

关键词： Reinforcement learning

Cover Picture: Materials and Corrosion. 11/2024

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Materials and Corrosion 2024年第11期75卷 1399-1399页

作者： Wei Zhang Mingyang Zhou Huifang Yue Danmin Peng Xi Qiu Jijun Yang Key Laboratory of Radiation Physics and Technology of Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China

Facility Layout in Horizontal Cylindrical Space Based on Adaptive Gradient Descent Algorithm

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Facility Layout in Horizontal Cylindrical Space Based on Ada...

第40届中国控制会议

作者： Peng Yuan Yiran Miao Deqing Huang Junkai Hao Na Qin School of Electrical Engineering Southwest Jiaotong University Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China

FLP（Facility layout problem） is a common problem in engineering field. The past research tend to focus on the twodimensional space and a small amount of three-dimensional box space as well as vertical cylinder space. The FLP in horizontal cylinder space is less researched. Lots of FLP of box space and vertical cylinder space can transform to FLP of two-dimensional space easily, however, the FLP of horizontal cylinder space need design special calculation method of overlap constraint and boundary constraint. This paper designed the calculation method of overlap constraint and boundary constraint, used adaptive gradient descent algorithm with heuristic strategy to optimize the facility layout in horizontal cylinder space. On this basis,added symmetry constraint, absolute height constraint and relative distance constraint to close to the real engineering *** experiment results confirm the feasible of proposed scheme.

关键词： facility layout problem horizontal cylinder space gradient descent algorithm

Numerical Trajectory Planning of Panel Installation with Optimal Energy Consumption for Redundant Articulating Crane

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SSRN

SSRN 2024年

作者： Li, Penghao Zhang, Bangji Zhang, Zheshuo Hu, Wen Zhang, Jie Dai, Di State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Intelligent Transportation System Research Center Hangzhou City University Hangzhou China School of Vehicle and Mobility Tsinghua University Beijing China College of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha China

The automatic installation of panels has become crucial in numerous industries scenarios. This paper proposed a trajectory planning method to addresses the position and posture adjustment of panel installation using redundant articulating cranes. Configuration space of the crane and rotation matrix are combined to investigate the strong coupling of the panel's position and posture. Moreover, the study combines the motion modes of the crane with the sequential analysis of redundant motion to assess energy consumption, incorporating energy efficiency as one of the planning objectives. Simultaneously, an intrinsic connection between the motion sequences within the C-space and safety considerations is analyzed. The installation experiment is performed with a real crane. The experimental results indicate that the proposed method achieves the function of installation of panels, which can make both the panel’s position and posture safely reach the predetermined state with less energy consume. © 2024, The Authors. All rights reserved.

关键词： Cranes

design,modeling and experimental investigation of a magnetically modulated rotational energy harvester for low frequency and irregular vibration

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science China(Technological sciences) 2020年第10期63卷 2051-2062页

作者： ZHAO LinChuan ZOU HongXiang GAO QiuHua YAN Ge WU ZhiYuan LIU FengRui WEI KeXiang YANG Bin ZHANG WenMing State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China Hunan Provincial Key Laboratory of Vehicle Power and Transmission System Hunan Institute of EngineeringXiangtan 411104China National Key Laboratory of Science and Technology on Micro/Nano Fabrication Shanghai Jiao Tong UniversityShanghai 200240China

Vibration energy harvesting is a promising approach for sustainable energy generation from ambience to meet the development of self-powered ***,we propose a novel compact non-resonant magnetically modulated rotational energy harvester(MMR-EH)for low frequency and irregular *** the rational arrangement of multiple magnetic fields in space,a ring route with low potential energy is established.A movable magnet can be non-contact modulated by the magnetic force to move along the ring route under irregular vibration,which is instrumental in electromechanical energy conversion.A dynamic model of the MMR-EH is developed based on the energy method and verified *** effects of key parameters on the magnetically modulated route are *** simulation and experimental results demonstrate that the MMR-EH can effectively harvest the energy from ultra-low frequency(3 Hz)and irregular *** a reciprocating vibration frequency of 10 Hz and an amplitude of 20 mm,the harvester can produce an average power of 0.29 mW.

关键词： energy harvesting vibration low frequency magnetically modulated route

Measurement-Based Channel Characteristics for Air-to-Ground Communications Under Rural Areas

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Measurement-Based Channel Characteristics for Air-to-Ground ...

European Conference on Antennas and Propagation, EuCAP

作者： Xuchao Ye Hanpeng Li Kai Mao Qiuming Zhu Farman Ali Xiao-min Chen Yanheng Qiu Hangang Li The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing Jiangsu China Qurtuba University of Science and Information Technology Dera Ismail Khan Pakistan

As an important part of the beyond fifth generation (B5G) and sixth generation (6G) network, unmanned aerial vehicle (UAV) communication has been widely used in various application scenarios. The measurement and analysis of UAV air-to-ground (A2G) channel characteristics is vital to the design of future UAV communication systems. In this paper, we introduce a channel sounder that supports real-time measurements but has smaller data storage sizes. The developed channel sounder is used to carry out channel measurement campaigns in two typical rural scenarios, i.e., farmland and river. Based on the measurement data, some key channel characteristics, including multipath components (MPCs) and path loss (PL), are analyzed and compared. The results can provide a valuable reference for UAV channel modeling and UAV communication system design in rural areas.

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