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NetChain: A Blockchain-Enabled Privacy-Preserving Multi-Domain Network Slice Orchestration Architecture

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第1期19卷 188-202页

作者： He, Guobiao Su, Wei Gao, Shuai Liu, Ningchun Das, Sajal K. Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Peng Cheng Lab PCL Res Ctr Networks & Commun Shenzhen 518066 Peoples R China Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

Multi-domain networking slice orchestration is an essential technology for the programmable and cloud-native 5G network. However, existing research solutions are either based on the impractical assumption that operators will reveal all the private network information or time-consuming secure multi-party computation which is only applicable to limited computation scenarios. To provide agile and privacy-preserving end-to-end network slice orchestration services, this paper proposes NetChain, a multi-domain network slice orchestration architecture based on blockchain and trusted execution environment. Correspondingly, we design a novel consensus algorithm CoNet to ensure the strong security, scalability, and information consistency of NetChain. In addition, a bilateral evaluation mechanism based on game theory is proposed to guarantee fairness and Quality of Experience by suppressing the malicious behaviors during multi-domain network slice orchestration. Finally, the prototype of NetChain is implemented and evaluated on the Microsoft Azure Cloud with confidential computing. Experiment results show that NetChain has good performance and security under the premise of privacy-preserving.

关键词： Security Computer architecture Privacy Scalability Blockchains Faces consensus algorithm Multi-domain network slicing privacy-preserving blockchain TEE

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Trusted Scalable SAT Solving with On-The-Fly LRAT Checking 27

Trusted Scalable SAT Solving with On-The-Fly LRAT Checking

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27th International Conference on Theory and Applications of Satisfiability Testing, SAT 2024

作者： Schreiber, Dominik Karlsruhe Institute of Technology Germany

ISBN: (纸本)9783959773348

Recent advances have enabled powerful distributed SAT solvers to emit proofs of unsatisfiability, which renders them as trustworthy as sequential solvers. However, this mode of operation is still lacking behind conventional distributed solving in terms of scalability. We argue that the core limiting factor of such approaches is the requirement of a single, persistent artifact at the end of solving that is then checked independently (and sequentially). As an alternative, we propose a bottleneck-free setup that exploits recent advancements in producing and processing LRAT information to immediately check all solvers’ reasoning on-the-fly during solving. In terms of clause sharing, our approach transfers the guarantee of a derived clause’s soundness from the sending to the receiving side via cryptographic signatures. Experiments with up to 2432 cores (32 nodes) indicate that our approach reduces the running time overhead incurred by proof checking by an order of magnitude, down to a median overhead of ≤ 42% over non trusted solving. © Dominik Schreiber.

关键词： consensus algorithm

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Edge Detection of Noise Images Based on Improved Canny algorithm with Adaptive Threshold 5

Edge Detection of Noise Images Based on Improved Canny Algor...

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5th International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2024

作者： Zhang, Jing Anhui Sanlian University School of Modern Health and Regimen Industry Hefei China

ISBN: (纸本)9798350355253

Edge detection is a significant research direction in the fields of image processing and computer vision, widely applied in image segmentation, object detection, and other areas. The traditional Canny edge detection algorithm excels in noise resistance and edge localization, but it still faces challenges such as edge information loss and the need for manual setting of dual thresholds when processing noisy images. This paper proposes an improved Canny edge detection algorithm with adaptive thresholds, which effectively addresses the deficiencies of the traditional Canny algorithm in noise suppression and threshold setting by introducing a wavelet filter and an adaptive threshold determination method based on Otsu's method for maximizing the between-class variance. Experimental results on the BSD500 dataset demonstrate that the proposed algorithm performs excellently in edge detection of noisy images, with an accuracy improvement of approximately 5%-10% compared to the traditional Canny algorithm. © 2024 IEEE.

关键词： consensus algorithm

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Community Hiding algorithm Based on Likelihood Analysis Method in Link Prediction 22

Community Hiding Algorithm Based on Likelihood Analysis Meth...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Ya-Si Shi, Lei-Lei Han, Zi-Xuan Liu, Lu Song, Siyang Chen, Liming Jiangsu University School of Computer Science and Communication Engineering China University of Leicester School of Computing and Mathematical Sciences United Kingdom Dalian University of Technology School of Computer Science and Technology China

ISBN: (纸本)9798331509712

In recent years, the rapid development and widespread applications of non-overlapping community discovery have led to a growing issue of privacy leakage. The core of this problem lies in the community discovery algorithms themselves. Researchers have shifted their focus toward developing community hiding algorithms. However, existing non-overlapping community hiding algorithms have mainly been studied in static networks, overlooking the prevalence of dynamic networks in real life. In this paper, we introduce a community hiding algorithm called LALH, which employs the link prediction likelihood analysis method. The algorithm consists of two parts: first, the OLPL algorithm calculates a list of link probabilities in the subsequent time scale of the network, allowing observation of important link distribution. Second, the LALH algorithm selects these crucial links for the community hiding operation. We validate the effectiveness of the LALH algorithm through experiments on three public datasets and a real Twitter dataset. © 2024 IEEE.

关键词： consensus algorithm

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Density Peaks Clustering algorithm Based on Weighted Shared Nearest Neighbors 5

Density Peaks Clustering Algorithm Based on Weighted Shared ...

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5th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Suo, Jinyi Xing, Aosheng Sun, Tonglei Chongqing Normal University School of Computer and Information Science Chongqing China

ISBN: (纸本)9798331506612

Density Peaks Clustering is a commonly utilized, effective, and non-iterative algorithm for clustering. However, studies have demonstrated that DPC faces challenges when dealing with non-spherical clusters and datasets with uneven density distributions, particularly in selecting appropriate cluster centers and its sensitivity to the cutoff distance parameter. To overcome the limitations of DPC in handling uneven density distributions, this paper introduces a Density Peaks Clustering algorithm Based on Weighted Shared Nearest Neighbors. This approach utilizes natural neighbors to determine weight values and redefines the similarity between data points by incorporating both first-order and second-order shared nearest neighbors. It then computes relative density and relative distance by integrating shared nearest neighbor similarities with natural neighbor weight values. Finally, the algorithm groups samples into clusters. Experiments conducted on six different types of datasets demonstrate that the proposed algorithm notably outperforms three benchmark algorithms. © 2024 IEEE.

关键词： consensus algorithm

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Distributed Estimation of Anomalies using a Low Complexity PSO algorithm

Distributed Estimation of Anomalies using a Low Complexity P...

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2024 International Conference on Emerging Trends in Smart Technologies, ICETST 2024

作者： Saeed, Muhammad Omer Bin Khan, Salman A. Butt, Naveed R. Mohammad, Nazeeruddin Zerguine, Azzedine Gik Institute Of Engineering Sciences & Technology Faculty Of Engineering Sciences Topi Pakistan Karachi Institute Of Economics & Technology College Of Computing & Information Sciences Karachi Pakistan Prince Mohammad Bin Fahd University College Of Computer Engineering And Science AlKhobar Saudi Arabia King Fahd University Of Petroleum & Minerals Department Of Electrical Engineering Dhahran Saudi Arabia

ISBN: (纸本)9798331515850

Any wireless sensor network can experience anomalies that can hinder performance. This work presents a distributed estimation algorithm that estimates any such anomalies using a PSO algorithm. PSO algorithms are computationally expensive. This work presents a low complexity version of the algorithm that removes redundant computations and achieves excellent performance as well. Low complexity algorithms are essential for a longer battery life of wireless sensor so this algorithm provides an excellent alternative to the basic linear adaptive filtering algorithms that have low complexity but require a lot more time to achieve similar results. © 2024 IEEE.

关键词： consensus algorithm

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Extending Instance Space Analysis to algorithm Configuration Spaces

Extending Instance Space Analysis to Algorithm Configuration...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Rasulo, Anthony Smith-Miles, Kate Muñoz, Mario Handl, Julia López-Ibáñez, Manuel The University of Manchester The University of Melbourne Manchester United Kingdom The University of Manchester Manchester United Kingdom

ISBN: (纸本)9798400704956

This paper describes an approach for deriving visual insights about the joint relationship between algorithm performance, algorithm parameters, and problem instance features. This involves the combined analysis and exploration of a 2D instance space, to which instances from some problem space are projected, and a 2D configuration space, to which (algorithm) configurations are projected. Extending on the dimensionality reduction problem solved in Instance Space Analysis, we define an optimisation problem for finding projections to these two spaces, with an interpretable relationship between them. Then, we describe the tools developed for probing those spaces in an investigation of the question: What characterises the algorithm configurations that perform best on a selected group of instances (or vice versa)? We demonstrate the use of these tools on synthetic data with known ground truth. © 2024 held by the owner/author(s).

关键词： consensus algorithm

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On the Second-Order Convergence of Biased Policy Gradient algorithms 41

On the Second-Order Convergence of Biased Policy Gradient Al...

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41st International Conference on Machine Learning, ICML 2024

作者： Mu, Siqiao Klabjan, Diego Department of Engineering Sciences and Applied Mathematics Northwestern University EvanstonIL United States Department of Industrial Engineering and Management Sciences Northwestern University EvanstonIL United States

Since the objective functions of reinforcement learning problems are typically highly nonconvex, it is desirable that policy gradient, the most popular algorithm, escapes saddle points and arrives at second-order stationary points. Existing results only consider vanilla policy gradient algorithms with unbiased gradient estimators, but practical implementations under the infinite-horizon discounted reward setting are biased due to finite-horizon sampling. Moreover, actor-critic methods, whose second-order convergence has not yet been established, are also biased due to the critic approximation of the value function. We provide a novel second-order analysis of biased policy gradient methods, including the vanilla gradient estimator computed from Monte-Carlo sampling of trajectories as well as the double-loop actor-critic algorithm, where in the inner loop the critic improves the approximation of the value function via TD(0) learning. Separately, we also establish the convergence of TD(0) on Markov chains irrespective of initial state distribution. Copyright 2024 by the author(s)

关键词： consensus algorithm

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An Optimal Randomized algorithm for Finding the Saddlepoint 32

An Optimal Randomized Algorithm for Finding the Saddlepoint

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32nd Annual European Symposium on algorithms, ESA 2024

作者： Dallant, Justin Haagensen, Frederik Jacob, Riko Kozma, László Wild, Sebastian Department of Computer Science Université libre de Bruxelles Belgium Department of Computer Science IT University of Copenhagen Denmark Institut für Informatik Freie Universität Berlin Germany Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9783959773386

A saddlepoint of an n × n matrix is an entry that is the maximum of its row and the minimum of its column. Saddlepoints give the value of a two-player zero-sum game, corresponding to its pure-strategy Nash equilibria;efficiently finding a saddlepoint is thus a natural and fundamental algorithmic task. For finding a strict saddlepoint (an entry that is the strict maximum of its row and the strict minimum of its column) an O(n log∗ n)-time algorithm was recently obtained by Dallant, Haagensen, Jacob, Kozma, and Wild, improving the O(n log n) bounds from 1991 of Bienstock, Chung, Fredman, Schäffer, Shor, Suri and of Byrne and Vaserstein. In this paper we present an optimal O(n)-time algorithm for finding a strict saddlepoint based on random sampling. Our algorithm, like earlier approaches, accesses matrix entries only via unit-cost binary comparisons. For finding a (non-strict) saddlepoint, we extend an existing lower bound to randomized algorithms, showing that the trivial O(n2) runtime cannot be improved even with the use of randomness. © Justin licensed.

关键词： consensus algorithm

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Resilient Distributed Optimization algorithm Based on Trusted Nodes against Malicious Attacks 43

Resilient Distributed Optimization Algorithm Based on Truste...

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43rd Chinese Control Conference, CCC 2024

作者： Cao, Linyao Gao, Wenhua South China University of Technology School of Mathematics Guangzhou China

ISBN: (纸本)9789887581581

Distributed optimization problem is a category of optimization problems on multi-agent systems. In contrast to centralized optimization, it does not require a central decision-maker and has higher openness and scalability, which is more conducive to solving large-scale complex optimization problems. However, at the same time, it is more susceptible to network attacks. To withstand malicious attacks, some scholars have introduced a fault-tolerance mechanism by constructing a connected dominating set (CDS) of trusted nodes within the network. The CDS governs network decision-making to enhance the system security. In this paper, we relax this construction and propose a novel fault-tolerant mechanism. This mechanism does not require the construction of a CDS but only ensures that each node is connected to at least one trusted node. By sacrificing a little iteration time, it combats malicious attacks and avoids the algorithm moving towards a centralized approach. Then the paper leverages the new fault-tolerant mechanism to propose a resilient distributed optimization algorithm based on trusted nodes. The effectiveness of the algorithm is verified by analyzing the consensus and convergence of it. Finally, numerical experimental results show that the performance of the proposed algorithm is almost consistent to the resilient distributed optimization algorithm based on the CDS. The proposed algorithm is effective and can still operate normally even when the trusted nodes do not form a CDS. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： consensus algorithm

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