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On the Trade-Offs of Combining Multiple Secure processing Primitives for Data Analytics 20th

On the Trade-Offs of Combining Multiple Secure Processing Pr...

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20th IFIP WG 6.1 international conference on distributed applications and Interoperable Systems (DAIS) Held as Part of the 15th international Federated conference on distributed Computing techniques (DisCoTec)

作者： Carvalho, Hugo Cruz, Daniel Pontes, Rogerio Paulo, Joao Oliveira, Rui INESC TEC Braga Portugal Univ Minho Braga Portugal

ISBN: (纸本)9783030503222;9783030503239

Cloud Computing services for data analytics are increasingly being sought by companies to extract value from large quantities of information. However, processing data from individuals and companies in third-party infrastructures raises several privacy concerns. To this end, different secure analytics techniques and systems have recently emerged. These initial proposals leverage specific cryptographic primitives lacking generality and thus having their application restricted to particular application scenarios. In this work, we contribute to this thriving body of knowledge by combining two complementary approaches to process sensitive data. We present SafeSpark, a secure data analytics framework that enables the combination of different cryptographic processing techniques with hardware-based protected environments for privacy-preserving data storage and processing. SafeSpark is modular and extensible therefore adapting to data analytics applications with different performance, security and functionality requirements. We have implemented a SafeSpark's prototype based on Spark SQL and Intel SGX hardware. It has been evaluated with the TPC-DS Benchmark under three scenarios using different cryptographic primitives and secure hardware configurations. These scenarios provide a particular set of security guarantees and yield distinct performance impact, with overheads ranging from as low as 10% to an acceptable 300% when compared to an insecure vanilla deployment of Apache Spark.

关键词： Data analytics Privacy Trusted hardware

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NFT-basedAccess Control in Named Data Networks

NFT-basedAccess Control in Named Data Networks

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Hui Zhao Xiaodong Zhang Ru Li College of Computer Science Inner Mongolia University Hohhot China

The information cache technology in the Named Data Network (NDN) decouples the location of the Content Provider (CP) from the data, which leads to the data in the storage nodes facing security threats due to the lack of security control. Therefore, access control becomes extremely important. Because Ciphertext-Policy Attribute-Based Encryption (CP-ABE) supports sending data to a group of unknown recipients and realizes flexible and fine-grained access control, CP-ABE is often used in the access control of NDN. However, there are two problems when CP-ABE is directly applied to NDN: first, CP is required to be online all the time to verify the legitimacy of users and distribute decryption keys; Second, illegal users can obtain ciphertext. In view of the above problems, we propose an access control scheme based on Non-Fungible Token(NFT). Smart contracts distribute AttributeNFT and AccessNFT for users, and provide credentials for users to obtain keys and ciphertext. NDN routers only forward ciphertext data packets to valid users by implementing the filtering mechanism based on AccessNFT, and finally achieve the purpose of protecting data and achieving the safe distribution of data. Finally, we implement the system prototype on the Hyperledger Fabric and analyze the performance of the smart contract with the caliper. The experimental results show that the access delay of the smart contract is low and can meet the requirements of access control.

关键词： Access control Filtering distributed ledger Smart contracts Prototypes Fabrics Encryption

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Rumor Has It: Optimizing the Belief Propagation Algorithm for parallel processing 20

Rumor Has It: Optimizing the Belief Propagation Algorithm fo...

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49th international conference on parallel processing (ICPP)

作者： Trotter, Michael Wood, Timothy Huang, H. Howie George Washington Univ Washington DC 20052 USA

ISBN: (纸本)9781450388689

By modelling how the probability distributions of individuals' states evolve as new information flows through a network, belief propagation has broad applicability ranging from image correction to virus propagation to even social networks. Yet, its scant implementations confine themselves largely to the realm of small Bayesian networks. applications of the algorithm to graphs of large scale are thus unfortunately out of reach. To promote its broad acceptance, we enable belief propagation for both small and large scale graphs utilizing GPU processing. We therefore explore a host of optimizations including a new simple yet extensible input format enabling belief propagation to operate at massive scale, along with significant workload processing updates and meticulous memory management to enable our implementation to outperform prior works in terms of raw execution time and input size on a single machine. Utilizing a suite of parallelization technologies and techniques against a diverse set of graphs, we demonstrate that our implementations can efficiently process even massive networks, achieving up to nearly 121x speedups versus our control yet optimized single threaded implementations while supporting graphs of over ten million nodes in size in contrast to previous works' support for thousands of nodes using CPU-based multi-core and host solutions. To assist in choosing the optimal implementation for a given graph, we provide a promising method utilizing a random forest classifier and graph metadata with a nearly 95% F1-score from our initial benchmarking and is portable to different GPU architectures to achieve over an F1-score of over 72% accuracy and a speedup of nearly 183x versus our control running in this new environment.

关键词： GPGPU parallelization graph processing parallel processing Bayesian Networks Markov Random Fields

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Novel Approach of Digit Serial Multiple Constant Multiplication in DSP applications

Novel Approach of Digit Serial Multiple Constant Multiplicat...

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Emerging Trends in Engineering and Medical Sciences (ICETEMS), international conference on

作者： Rajendra M. Rewatkar Chetan G. Puri Amit A. Gudadhe Shubhangi V. Rathakanthiwar Nitin Wange Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi (Meghe) Wardha India Faculty of Engineering and Technology FEAT Datta Meghe Institute of Higher Education and Research Sawangi (Meghe) Wardha India Department of Electronics Engineering Yashwantrao Chavan College of Engineering Nagpur India Department of Mathematics Yashwantrao Chavan College of Engineering Nagpur India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Paper Presents optimization technique for adder, multiplier and FIR filter using different architectures. Most important part of a DSP processor is the multiplier and accumulator. Multiplication is a heavy weighted process in computation and hardware, whereas adder is a light weight but important unit in any processor which basically leads to a number of operations like addition, subtraction, increment, decrements etc. Digit-serial operation is best suited for execution of DSP systems which need moderate sampling rates. This digit serial architecture allows bit-level pipelining, thus realizing sample speeds corresponding to bit-parallel multipliers showing less area. Paper presented a novel approach of digit-serial multiplier styles with MCM. In this research, the valuation of complex pathway and power dissipation of different digit serial multipliers besides their deviation at various digit sizes is discovered. At last, FIR Filter with Digit Serial Architecture along with MCM is designed through Active-HDL and Xilinx The results for power, area and speed are tested and a report is included. Attention has been given to the digit serial design which offers alternate low complication in processes. Investigational observations have exposed the effectiveness of the projected method and the circuit of digit serial methods causes low power consumption of FIR filters. Digit serial techniques with MCM improve the essential parameters in DSP implementation.

关键词： Power demand Finite impulse response filters Computer architecture Market research Hardware Power dissipation Hardware design languages Adders Optimization Pipeline processing

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Distributional Knowledge Transfer for Heterogeneous Federated Learning

Distributional Knowledge Transfer for Heterogeneous Federate...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Huan Wang Lijuan Wang Jun Shen School of Cyber Engineering Xidian University Xi'an China School of Computing & Information Technology University of Wollongong Wollongong Australia

Federated learning (FL) produces an effective global model by aggregating multiple client weights trained on their private data. However, it is common that the data are not independently and identically distributed (non-IID) across different clients, which greatly degrades the performance of the global model. We observe that existing FL approaches mostly ignore the distribution information of client-side private data. Actually, the distribution information is a kind of structured knowledge about the data itself, and it also represents the mutual clustering relations of data examples. In this work, we propose a novel approach, namely Federated Distribution Knowledge Transfer (FedDKT), that alleviates heterogeneous FL by extracting and transferring the distribution knowledge from diverse data. Specifically, the server learns a lightweight generator to generate data and broadcasts it to the sampled clients, FedDKT decouples the feature representations of the generated data and transfers the distribution knowledge to assist model training. In other words, we exploit the similarity and shared parts of the generated data and local private data to improve the generalization ability of the FL global model and promote representation learning. Further, we also propose the similarity measure and attention measure strategies, which implement FedDKT by capturing the correlations and key dependencies among data examples, respectively. The comprehensive experiments demonstrate that FedDKT significantly improves the performance and convergence rate of the FL global model, especially when the data are extremely non-IID. In addition, FedDKT is also effective when the data are identically distributed, which fully illustrates the generalization and effectiveness of the distribution knowledge.

关键词： Training Representation learning Knowledge engineering Correlation Federated learning distributed databases Data models

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WGeod: A General and Efficient FPGA Accelerator for Object Detection

WGeod: A General and Efficient FPGA Accelerator for Object D...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Zihan Wang Mengying Zhao Lei Gong Chao Wang School of Computer Science and Technology University of Science and Technology of China University of Science and Technology of China Suzhou Institute for Advanced Research School of Computer Science and Technology Shandong University wangzh

With the development of high-performance embedded applications, object detection algorithms are starting to be deployed to embedded devices with fewer resources, and the classification and location information they provide is the basis for making further decisions in some high-performance embedded applications, so real-time and low-power consumption are especially needed. In this paper, we propose an FPGA-based neural network ac-celerator for object detection, which improves the speed, throughput and power efficiency of the inference with little loss of accuracy. The accelerator balances the speed difference between computation and memory access. The accelerator is scalable and can be configured for CNN of any size, and is general enough to support the acceleration of YOLOv2, YOLOv3 and their Tiny versions. We deploy the Tiny YOLOv3 network on FPGA-based accelerator, CPU and GPU platforms, and compare them in terms of speed, throughput and power efficiency. For the Tiny YOLOv3 network, the accelerator delivers an FPS of 1.61 and throughput of 8.95 GOP/s, which is 23.81x, 4.11x and 3.00x better than the Intel Core i5-10210U CPU with the 00, 01 and O3 compilation optimization options respectively. With 2.11 W of power, the accelerator delivers a power efficiency of 4.24 GOP/s/W, which is 141.33x, 28.27x and 21.20x better than the Intel Core i5-10210U CPU with the 00, O1 and O3 options respectively.

关键词： Neural networks Graphics processing units Object detection Throughput Real-time systems Classification algorithms Optimization

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Underwater LED light MIMO telecentric optical path technology research

Underwater LED light MIMO telecentric optical path technolog...

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IEEE international Symposium on parallel and distributed processing with applications and IEEE international conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jiangnan Liu Guojin Peng Jiapeng Su Provincial Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin China

The system capacity can be further improved by building Light Emitting Diode (LED) light Multiple-Input Multiple-Output (MIMO) on the basis of hemispherical array structure, but the MIMO capacity is limited by the degree of sub-channel interference. To reduce the sub-channel interference of hemispherical arrays, this paper proposes a method to control the sub-channel interference of LED optical MIMO based on the telecentric optical path, which can achieve the sub-channel isolation of LED optical MIMO in hemispherical array structure by using the restriction effect of the optical diaphragm on stray light and maintain the stable sub-channel isolation in a large communication range. Compared with the conventional Singular Value Decomposition (SVD)/Space-time Block Coding (STBC)/ optical zoom and other electrical and optical domain LED optical MIMO sub-channel interference control methods, the proposed method has better Bit Error Ratio (BER) performance and lower structural complexity, and improves the robustness of sub-channel correlation to communication distance variation.

关键词： Stimulated emission Interference Stray light Light emitting diodes Ubiquitous computing Adaptive optics Encoding

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distributed Secure Services Based on IoT and Blockchain for e-Health Remote Care 2

Distributed Secure Services Based on IoT and Blockchain for ...

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2nd international conference on Computer Science's Complex Systems and their applications, ICCSA 2021

作者： Reffad, Hamza Djenaoui, Abdelatif Alti, Adel Computer Science Department Lrsd Laboratory University Ferhat Abbas Sétif-1 Sétif19000 Algeria

Nowadays, the Internet of Thing (IoT) is a potentially powerful solution for eHealth applications. It is a smart technology that provides remote care in real time and requires low latency health data processing and transmission. The large number of connected objects to Cloud can be a problem for low-latency workloads, which is the case of several health mobile applications. To this end, Fog Computing, has emerged, where Cloud computing is extended to the edge of the network to reduce latency and network congestion. It provides a highly virtualized platform that provides health data storage on remote public Cloud servers to which users cannot be fully trusted, especially when we are dealing with sensitive data like health data. In fact, it becomes necessary to rethink a new more robust secure technique. To provide such technique, we proposed a new secure solution called IoToDChain for e-Health mobile application, based on cryptographic techniques especially Elliptic Curve Diffie Hellman-RSA and the Blockchain paradigm. They exchange of a secret key in confidential and robust manner and protect patients' privacy in a mobile-Fog-Cloud environment. The experiments achieved promising results for good data protection against the most known attacks in healthcare systems. © 2020 Copyright for this paper by its authors.

关键词： Smart contract

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Channel Modeling and Signal processing for Underwater Massive MIMO System Under Link Misalignment

Channel Modeling and Signal Processing for Underwater Massiv...

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IEEE international Symposium on parallel and distributed processing with applications and IEEE international conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Kongliang Zhu Yanlong Li Jielin Fu Guojing Peng Department of Information and Communication Guilin University of Electronic Technology Guilin China Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin China

Aiming at the problem of increased interference of different light signals caused by the increase in the number of LEDs, the link misalignment of the underwater optical wireless communication (UOWC) system, and the shift and expansion of the light spot in the massive multiple in multiple out (Massive-MIMO) optical communication system, this paper uses an aspherical lens set as the receiving optical antenna to separate different beams and optimizes the lens structure to further completely separate different beams. In this paper, the Singular value decomposition based on the least-squares channel estimation (LS-SVD) precoding algorithm is used to reduce the impact of communication link misalignment on the performance of the communication system and improve the system bit error rate performance (BER). The simulation results show that the LS-SVD precoding algorithm is better than the traditional Lattice Reduction (LR) precoding algorithm, zero-forcing (ZF) detection algorithm, and the minimum mean square error (MMSE) detection algorithm under the condition of misalignment of the communication link in the underwater Massive-MIMO system, the LS-SVD precoding algorithm improves the BER by about an order of magnitude under the same signal-to-noise ratio (SNR).

关键词： Precoding Simulation Bit error rate Signal processing algorithms Interference Optical fiber communication Detection algorithms

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CNNBooster: Accelerating CNN Inference with Latency-aware Channel Pruning for GPU

CNNBooster: Accelerating CNN Inference with Latency-aware Ch...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Yuting Zhu Hongxu Jiang Runhua Zhang Yonghua Zhang Dong Dong Beijing Key Laboratory of Digital Media of Beihang University Beihang University Beijing China

Channel pruning is one of the mainly used meth-ods in current network model compression. However, existing channel pruning methods lack effective hardware runtime latency guidance, making the reduction in model size not fully converted into inference latency reduction, which results in poor inference performance of the pruned network models. This paper proposes a CNN channel pruning framework, CNNBooster, that incorporates hardware runtime latency in-formation, whose two major contributions are as follows: First, CNNBooster automatically analyses the latency behavior of various CNNs with channel reduction on different GPU hard-ware platforms, which achieves efficient localization of pruna-ble coordinates. Second, CNNBooster uses a flexible grained latency-aware and param-aware pruning algorithm based on prunable coordinates to achieve a significant reduction in model inference latency and parameter size. We evaluate CNNBooster with three benchmark models (VGG16, ResNet18 and MobilenetV1) on three NVIDIA-series platforms (V100, RTX 2080 Ti and Jetson Nano). The experimental results show that CNNBooster can achieve a maximum 67.51 % latency re-duction and 1.4x performance improvement compare with current state-of-the-art works.

关键词： Location awareness Runtime Graphics processing units Benchmark testing Hardware Inference algorithms Behavioral sciences

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