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Secure CNN Training and Inference based on Multi-key Fully Homomorphic Encryption 29

Secure CNN Training and Inference based on Multi-key Fully H...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Qin, Hong He, Debiao Feng, Qi Luo, Min Wuhan University School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350330717

Convolutional neural network (CNN) has attracted increasing attention and been widely used in imaging processing, bioinformatics and so on. As the cloud computing and multiparty computing are booming, the training and inference data of convolutional neural network often comes from diverse users. These users tend to jointly perform the computation but reluctantly share original data with others. Multi-key fully homomorphic encryption (MKFHE) supports homomorphic computation on ciphertexts encrypted with different keys, which is especially suitable for this scenario. In this paper, we firstly propose secure convolution, matrix multiplication, comparison and maximum protocols based on MKFHE. Then we design the secure CNN training and inference framework, outsourcing almost all computations to cloud server. To improve the efficiency, we use key switching technique for ciphertext transformation. We prove that the proposed frameworks are secure and feasible. The theoretical and experimental analysis show that our framework achieves the trade-off between security, efficiency and scalability. © 2023 IEEE.

关键词： Cloud computing Convolutional neural network Multi-key fully homomorphic encryption Privacy protection

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Parareal with a Physics-Informed Neural network as Coarse Propagator 29th

Parareal with a Physics-Informed Neural Network as Coarse Pr...

引用

29th International conference on parallel and distributed Computing (Euro-Par)

作者： Ibrahim, Abdul Qadir Goetschel, Sebastian Ruprecht, Daniel Hamburg Univ Technol Inst Math Chair Computat Math Hamburg Germany

ISBN: (纸本)9783031396977;9783031396984

parallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that Parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves Parareal's single-node performance. This suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures.

关键词： Parareal parallel-in-time integration PINN Machine learning GPUs heterogeneous architectures

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Radio Environment Map based Routing Protocol for UAV networks 29

Radio Environment Map Based Routing Protocol for UAV Network...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Zheng, Siyue Zhu, Xiaojun Qin, Zhenrui Dong, Chao Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Northwest Missouri State University School of Computer Science and Information Systems United States Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering China

ISBN: (纸本)9798350330717

Unmanned Aerial Vehicles (UAVs), now often deployed on battlefields, use wireless networks to communicate with each other. Such wireless communication, however, may become unreliable under the jamming from opponents, and existing UAV routing protocols perform poorly when dealing with the jamming. In this paper, we propose to use the spectrum jamming prediction data from the radio environment map (REM) to update the network topology. The key is to model the network as a weighted graph where the weights of the edges between two neighboring nodes are functions of the location of the jammer. We then update the routing paths accordingly in real-time. The protocol can be incorporated with existing table-driven protocols. We implement the protocol in a commercial network simulator. Simulations show that our routing protocol improves the packet delivery ratio and throughput of existing table-driven routing protocols. © 2023 IEEE.

关键词： Radio Environment Maps routing protocol spectrum jamming UAV ad hoc network

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WAN-INT: Cost-Effective In-Band network Telemetry in WAN With A Performance-aware Path Planner 29

WAN-INT: Cost-Effective In-Band Network Telemetry in WAN Wit...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Chen, Simian He, Dongbiao Ma, Xiaopeng Ming, Zhongxing Cui, Laizhong Sangfor Technologies Inc China China National Tobacco Corporation Anhui Province China Shenzhen University College of Computer Science and Software Engineering China China

ISBN: (纸本)9798350330717

With the development of cross-datacenter services, accurate and low-cost network performance measurement enables better traffic scheduling. However, the existing network measurement suffers from a lack of telemetry granularities and excessive costs. Besides, the implementation of INT in the WAN remains undefined. In this work, We propose WAN-INT, a two-phase path orchestration algorithm designed to address the challenges posed by limited communication resources and varying link states in WAN scenarios. By generating a telemetry policy based on this algorithm, it can effectively adapt to the requirements of various applications and network states, achieving a balance between telemetry quality and cost limitations. We conduct experiments in a commercial WAN environment. Results show that WAN-INT outperforms existing schemes and reaches a good compromise between the telemetry quality and cost constraints. Compared with the state-of-the-art telemetry system, WAN-INT effectively reduces by at least 43% of telemetry cost while ensuring telemetry quality. © 2023 IEEE.

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Throughput Optimization VNF Placement in Cloud Datacenter Considering Time-Varying Workload and Multi-Tenancy 29

Throughput Optimization VNF Placement in Cloud Datacenter Co...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Yue, Yi Sun, Shiding Zhang, Zhiyan Tang, Xiongyan Yang, Wencong Zhang, Xuebei China Unicom Research Institute Beijing China National Engineering Research Center of Next Generation Internet Broadband Service Application Beijing China Zhengzhou University School of Electrical Engineering Zhengzhou China

ISBN: (纸本)9798350330717

network service providers benefit greatly from network Function Virtualization (NFV), which allows them to outsource their network Functions (NFs) to cloud data centers flexibly. This paper focuses on the Virtual network Function (VNF) placement in cloud data centers while maximizing the network's accepted Service Function Chain Requests (SFCRs). To optimize resource utilization, we consider two key factors that are often overlooked: time-varying workloads and VNF sharing based on multi-tenancy technology. We formulate the VNF placement problem as an Integer Linear Programming (ILP) model. To solve the ILP, we devise a Throughput Optimization Heuristic Solution (TOHS). Finally, we conduct a detailed numerical simulation and compare our results with contrasting schemes in the existing literature. Our evaluation shows that the performance of TOHS is near to results derived by ILP solver for small-scale problems. In addition, TOHS outperforms other solutions in various scenarios, resulting in higher network throughput and better utilization of network resources. © 2023 IEEE.

关键词： network Function Virtualization Resource allocation Service Function Chain Throughput optimization Virtual network Function

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PI-Parking: A Physics Informed Neural network Approach for Parking Availability Prediction 29

PI-Parking: A Physics Informed Neural Network Approach for P...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Dong, Yijun Kuang, Haoxuan Li, Jun Sun Yat-Sen University School of Intelligent Systems Engineering Shenzhen China

ISBN: (纸本)9798350330717

Parking availability prediction is an important part of urban intelligent transportation systems (ITS), which allows drivers to be informed of the parking availability in advance, thereby reducing cruising for parking time and reducing pollution emissions. However, existing studies of parking availability prediction rarely incorporate the effect of price factors and cannot be trained to obtain the price elasticity of parking demand. In this paper, we propose a neural network-based approach called physics-informed parking (PI-parking). The model employs physics-informed neural networks (PINNs) to add price information into model training, which not only achieves the parking availability prediction, but also calculates the price elasticity of demand of different parking lots during the training process. On the SF Park project, experiment results show that our model outperforms 17.711% on average than compared models. For price elasticity of parking demand, we find that the values are distributed between 0.02 and 0.31, and most of the values are between 0.25 and 0.27. In addition, there is a similarity in the elasticity values for adjacent neighborhoods. © 2023 IEEE.

关键词： intelligent transportation systems parking availability prediction physics-informed neural networks

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Reputation-based Streamlet: An Enhanced BFT Consensus Algorithm for Improved Performance and Scalability 29

Reputation-Based Streamlet: An Enhanced BFT Consensus Algori...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Fang, Bo Zhao, Dancheng Wang, Taotao Wang, Hao Zhang, Shengli Yang, Qing Monash University Department of Data Science and Artificial Intelligence Melbourne Australia Shenzhen University Blockchain Technology Research Center College of Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9798350330717

This work presents Reputation-based Streamlet (RBStreamlet), an improved version of the Streamlet consensus algorithm, designed to address limitations such as high bandwidth load and high communication complexity during the Propose and Vote phases. RBStreamlet introduces a reputation mechanism for leader election and fork selection, along with erasure coding and Merkle trees to reduce data transmission volume in the Propose phase. Furthermore, RBStreamlet leverages threshold signature technology to decrease the message complexity in the Vote phase. We implement the proposed RBStreamlet algorithm in a prototype blockchain system and build a network emulator to evaluate its performance under conditions resembling a practical wide-area network. Through extensive experiments, RBStreamlet demonstrates superior performance, scalability, and resilience to Byzantine attacks compared to the original Streamlet, especially in bandwidth-limited scenarios. These findings suggest its potential for practical applications in blockchain systems. © 2023 IEEE.

关键词： blockchain Byzantine fault tolerance consensus algorithm reputation streamlet

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Variational Graph Autoencoder based on High-Order Attention Mechanism 29

Variational Graph Autoencoder Based on High-Order Attention ...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Ge, Liang Lin, Yiping Li, Haifeng Xie, Junwei Chongqing University College of Computer Science Chongqing China

ISBN: (纸本)9798350330717

network embedding aims to learn low-dimensional representations of nodes by integrating the graph structure and node attribute information. In recent years, high-order information preservation network embedding has attracted increasing attention and many works have explored high-order structural proximity by combining random walks with shallow models. However, real-world networks are often complex, and shallow models cannot capture deep nonlinear information in the network, resulting in unreliable embeddings. Therefore, we propose a variational graph autoencoder based on a high-order attention mechanism (VGAEHA) to capture complex graph structure and node attribute information. VGAEHA obtains the high-order structural and attribute proximity of the graph through random surfing and integrates high-order information into each layer's attention calculation, which broadens the receptive field of each layer of the deep learning model and mitigates the over-smoothing problem. We further optimize the decoder to reconstruct both the structure and node attributes of the graph, greatly improving the model's representation ability. This addresses the issue where most existing graph variational autoencoder methods only focus on reconstructing the structural information during decoding, without fully exploiting the benefits of low-dimensional representation information. Experimental results on real datasets show that the VGAEHA achieves state-of-the-art performance on link prediction, node classification, and visualization. © 2023 IEEE.

关键词： Attention mechanism Attributed network embedding High-order information

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TSFCloNet: Clothing Classification Algorithm based on Two-Stream network Structure 29

TSFCloNet: Clothing Classification Algorithm Based on Two-St...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Jiang, Minghua Liu, Xiaoxiao Zhao, Yaxin Liu, Li Yu, Feng Wuhan Textile University School of Computer Science and Artificial Intelligence Wuhan China

ISBN: (纸本)9798350330717

In the fashion field, with the increasing diversity of clothing types and styles, accurate clothing classification becomes very important. However, the complex background and diverse styles of clothing images bring challenges to feature extraction. Classification based on texture features alone may focus too much on details and ignore the overall shape information, thus reducing the accuracy and stability of classification. In order to achieve fast and accurate clothing classification, this paper proposes a two-stream network structure clothing classification algorithm based on shape texture features and multi-feature fusion (TSFCloNet). Its main core is as follows: 1) using the two-stream network structure to extract texture and shape features from the input data set respectively;2) in the shape feature extraction stream, the clothing shape acquisition module is first used to process the input clothing data set, and the obtained clothing shape data set is input into the ShapeNet feature extraction module to obtain shape feature information;3) the FFCE (Feature Fusion Channel Enhancement) module is used to fuse the features obtained by the two branches of the structure respectively, and the DSAConv module is used to enhance feature extraction, and the final features are sent to the trained classifier to obtain the clothing style classification results. A large number of experimental results show that the proposed TSFCloNet network achieves higher classification accuracy when dealing with diverse and changeable fashion styles, significantly improving the performance of fashion image classification. © 2023 IEEE.

关键词： attention mechanism clothing style classification feature extraction feature fusion style

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Efficient Storage and Retrieval of Similar Data in Edge Computing Systems 29

Efficient Storage and Retrieval of Similar Data in Edge Comp...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Liu, Yuanfeng Xie, Junjie Liao, Hanlong Chen, Sheng Liu, Xiulong Guo, Deke Tianjin University College of Intelligence and Computing Tianjin300350 China AMS Institute of Systems Engineering Beijing100141 China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha410073 China

ISBN: (纸本)9798350330717

Edge computing is migrating services from remote clouds to the network edge, where a vast amount of data is also flowing into edge nodes. In this context, the Edge Data-Sharing System (EDSS) enhances service quality by enabling edge nodes to cooperate. However, the EDSS is suitable for precise search and faces the existing high overhead when many users retrieve similar data. To solve the obstacle, this paper proposes a similarity-based edge storage system, SESS, which leverages the software-defined edge network to realize efficient storage and retrieval of similarity data. We first design RealminHash, a core module of SESS, for efficient signature and and index for each data. Then, SESS calculates the storage strategy based on the similarity between data. Importantly, SESS adjusts this strategy using periodic network information to ensure load balancing. Experimental results demonstrate that SESS realizes the nearest-neighbor storage while maintaining load balancing. SESS outperforms the well-known k-means and spectral clustering methods in terms of accuracy and latency and supports millisecond similar queries. © 2023 IEEE.

关键词： Edge computing LSH minHash similarity-based storage software-defined edge network

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