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检索条件"任意字段=Proceedings of the 22nd International Conference on Distributed Computing and Networking"

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Let's wait awhile: how temporal workload shifting can reduce carbon emissions in the cloud 21

Let's wait awhile: how temporal workload shifting can reduce...

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proceedings of the 22nd international Middleware conference

作者： Philipp Wiesner Ilja Behnke Dominik Scheinert Kordian Gontarska Lauritz Thamsen Technische Universität Berlin Berlin Germany University of Potsdam Potsdam Germany

ISBN: (纸本)9781450385343

Depending on energy sources and demand, the carbon intensity of the public power grid fluctuates over time. Exploiting this variability is an important factor in reducing the emissions caused by data centers. However, regional differences in the availability of low-carbon energy sources make it hard to provide general best practices for when to consume electricity. Moreover, existing research in this domain focuses mostly on carbon-aware workload migration across geo-distributed data centers, or addresses demand response purely from the perspective of power grid stability and *** this paper, we examine the potential impact of shifting computational workloads towards times where the energy supply is expected to be less carbon-intensive. To this end, we identify characteristics of delay-tolerant workloads and analyze the potential for temporal workload shifting in Germany, Great Britain, France, and California over the year 2020. Furthermore, we experimentally evaluate two workload shifting scenarios in a simulation to investigate the influence of time constraints, scheduling strategies, and the accuracy of carbon intensity forecasts. To accelerate research in the domain of carbon-aware computing and to support the evaluation of novel scheduling algorithms, our simulation framework and datasets are publicly available.

关键词： carbon-aware scheduling data center resource management green computing temporal workload shifting

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LiPI: Lightweight Privacy-Preserving Data Aggregation in IoT

LiPI: Lightweight Privacy-Preserving Data Aggregation in IoT

引用

IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Himanshu Goyal Krishna Kodali Sudipta Saha School of Electrical Sciences Indian Institute of Technology Bhubaneswar India

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The ability of IoT devices to sense and share various physical parameters plays a key role in a smart system. Along side benefits, it also bears the potential to cause a breach of privacy for the users of the smart-systems. Existing solutions for privacy-preserving computation for distributed systems either extensively use highly complex cryptographic techniques or exploit an extremely high degree of message passing among the devices through secure channels. However, for the resource-constrained IoT devices, which compose a significant fraction of the smart-systems, the existing solutions for privacy-preserving computation strategies does not fulfill the current requirements. To address this issue, in this work, we propose a novel real-time lightweight strategy LiPI for Privacy-Preserving Data Aggregation in low-power IoT systems. LiPI uses lightweight distributed and collaborative data obfuscation, which substantially minimizes the computation requirements. In addition, it exploits the recent advances in Synchronous-Transmission (ST)-based protocols to efficiently fulfill the communication requirements too, making it efficient to work in real-time. Furthermore, LiPI also avoids dependency on any trusted third party. Extensive evaluation based on comprehensive experiments in both simulation platforms and publicly available WSN/IoT testbeds demonstrates that our strategy achieves the goal at least 51.7% faster and consumes 50.5% lesser energy compared to the existing state-of-the-art strategies.

关键词： Data privacy Wireless sensor networks Privacy Protocols distributed databases Collaboration Data aggregation

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TrustedBench: An Efficient and User-friendly distributed Performance Testing Tool for Blockchain System

TrustedBench: An Efficient and User-friendly Distributed Per...

引用

IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yang Cheng Kai Wei Yihui Zhang Chunyu Jiang Weiwei Pang Qi Zhang Bin Liu Lifeng Zhang Tingting Liu Yinqian Wu Cloud. Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Performance is a crucial indicator for the blockchain system evaluation. However, it is difficult to measure and comprehensively evaluate the performance of blockchain systems, particularly due to their complexity and diversity. Based on the comprehensive analysis of common blockchain systems and existing performance testing tools, this paper proposes a novel efficient and user-friendly performance testing tool for blockchain systems named TrustedBench. This tool supports all-round analysis and testing, and provides a powerful tool for understanding and analyzing the performance of blockchain systems. The architecture design, test execution process and important performance indicators of blockchain systems are introduced, and their calculation methods are further expounded. Finally, the overall results from the implementation of TrustedBench are presented and interpreted, revealing the operation and performance characteristics of some different blockchain products. With this tool, users can have a more comprehensive understanding of the performance of blockchain systems. Furthermore, TrustedBench effectively enhances the efficiency of the blockchain platform in many real-world scenarios.

关键词： Measurement Privacy System performance Computer architecture Blockchains Complexity theory Security

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An Automatic Generation Method for Path Expressions of CPN Model Focus on Tested Behaviors

An Automatic Generation Method for Path Expressions of CPN M...

引用

international conference on Computer Supported Cooperative Work in Design

作者： Sun Tao Linjing Zhang Huiping Ma College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9781538614839

With the gradually development of distributed software, collaborative work became a trend. At the same time, due to the continuous improvement of software complexity, software testing is highly susceptible to the state space explosion, and it also makes the formation of the path expressions for test behaviors become very difficult. For the above problem, a new algorithm of path expressions automation generation which based on Colored Petri Nets (CPN) is proposed in this paper. First, a new coverage criterion for parallel software testing based on transition coverage is proposed in this paper. Second, searching and optimizing the FullPath according these coverage criteria. FullPath is a set of paths that from initial elements to tested target in here. Third, extract and integrate all the arc expressions on these paths, and the results expressions only about the output arc variable of the initial position. Next, analyzing logical relation between different nodes on these paths and get the final path expressions. These path expressions combine all the information of arc expressions, guard expressions and constraint relationship of the CPN model. In particular, we need to consider the synchronization relationship of the transition, i.e., we need to consider the equivalent problem of the same name variable on the different output arc. According to the path expressions, we could obtain that the token of current place whether to reach the tested place. And, the tester also could get the change of token in tested place. Besides, these expressions could reduce the redundant path maximally and ensure effective coverage, so, it could ensure correctness and sufficiency of the test. It could be used to guide tester analyze the flow of tokens and remove redundancy testing dataset. Finally, the analysis and demonstration of the examples show that this method is reasonable and effective.

关键词： Software Software testing Finite element analysis Concurrent computing Computer science Electronic mail

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FedRSM: Representational-Similarity-Based Secured Model Uploading for Federated Learning

FedRSM: Representational-Similarity-Based Secured Model Uplo...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Gengxiang Chen Sheng Liu Xu Yang Tao Wang Linlin You Feng Xia School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen China School of Computing Technologies Royal Melbourne Institute of Technology Melbourne Australia

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

As a novel learning paradigm, Federated learning (FL) aims at protecting privacy by avoiding raw data shifting between distributed clients and central servers. However, recent researches demonstrate the vulnerability of FL against gradient-based privacy attacks, in which, gradients intercepted by malicious adversaries may result in data leakage. Current defense methods suffer from performance drops, low privacy guarantees, and high communication costs. Motivated by this, we propose FedRSM, a Representational-Similarity-Based Secured Model Uploading for Federated Learning. FedRSM splits Deep Neural Networks (DNNs) into layers, calculates Representational Dissimilarity Vector (RDV), measures the similarity between local RDV and global RDV of each model layer, and constructs secured local model to be uploaded based on Representational Consistency Alteration (RCA). According to the evaluation result, FedRSM can improve testing accuracy by up to 2%, significantly reduce communication costs, and avoid data leakage under different model complexities.

关键词： Training Data privacy Adaptation models Costs Federated learning Computational modeling Data models

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A Commitment and Ring Signature based Scheme for Amount and Identity Privacy Protection in Blockchain

A Commitment and Ring Signature based Scheme for Amount and ...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Shiyong Huang Haocong Li Ruoting Xiong Wei Ren Jie He Yi Ren School of Computer Science China University of Geosciences Wuhan China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University China School of Computer Science Beijing Jiaotong University Beijing China School of Computing Science University of East Anglia Norwich UK Beijing Key Laboratory of Urban Spatial Information Engineering Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Blockchain has been envisioned as an anonymous cryptocurrency framework and can be applied in various applications such as e-payment, share economics, and distributed ledger. Although an account is anonymous, privacy in terms of consumption behaviors still imposes leakage risks. For example, an adversary may infer the consumption capability related to an account further by analyzing the history of consumption records. It is thus of critical importance to design a solution to preserve the anonymity of both transaction amount and transaction peer’s identity, which is changeable because the amount must be still authenticated in anonymity. In this paper, we propose a scheme by using Pedersen commitment to anonymize the transaction amount, and together using ring signature to conceal the transaction peer’s identity while maintaining authentication. Especially, we further improve the security of anonymous authentication enabled by ring signature by introducing accountability, which can defend against double-spending attacks by penalization and empower auditability. The extensive performance and security analysis justify the applicability of the proposed scheme.

关键词： Economics Privacy distributed ledger Authentication Blockchains Information filtering Security

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Reference Architecture for a Collaborative Predictive Platform for Smart Maintenance in Manufacturing

Reference Architecture for a Collaborative Predictive Platfo...

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IEEE international conference on Intelligent Engineering Systems

作者： Z. Balogh E. Gatial J. Barbosa P. Leitao T. Matejka Department of Parallel and Distributed Computing Institute of Informatics Bratislava Slovakia Polytechnic Institute of Bragança Bragança Portugal Mat-obaly s.r.o. Prievidza Slovakia

Maintenance is a key factor to ensure the production efficiency, since the occurrence of unexpected failures leads to a degradation of the system performance, causing the loss of productivity and business opportunities, which are crucial roles to achieve competitiveness. The article aims to propose a reference architecture which will improve the way maintenance is considered in the current manufacturing world, by enabling an overall increase of production rates, while increasing the operational equipment effectiveness and decreasing the impact of maintenance needs. This objective would be accomplished by establishing an IoT infrastructure for the collection of the huge amount of available shop floor data, which can be analyzed, considering data analytics algorithms, predictive maintenance models and forecasting techniques, to perform the machine/system health assessment and prediction of maintenance needs, e.g. by detecting earlier the occurrence of possible failures and consequently the need to implement maintenance interventions. The scheduling of predictive maintenance needs will be integrated with the existing maintenance planning tools, and especially synchronized with the production planning tools to achieve a nondisruptive maintenance impact in the production system. A cloud-based collaborative maintenance services platform allows the secure collection, aggregation and analysis of a large amount of shared data from numerous manufacturers that use the same or similar machinery, and acts as an open market where companies can contract specialized maintenance services. This reference architecture aims to provide replicable architecture to be broadly applicable in a variety of industries, capable to improve the production efficiency through a real-time health monitoring and early detection of failures and outages, to speed up the maintenance delivery, and consequently mitigate their impact.

关键词： Predictive maintenance Manufacturing Collaboration Production facilities Sensors

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Differential Privacy Frequent Closed Itemset Mining over Data Stream

Differential Privacy Frequent Closed Itemset Mining over Dat...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Xuebin Ma Shengyi Guan Yanan Lang Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Inner Mongolia University Hohhot China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

As the most common technique for mining and analyzing massive data, frequent itemset mining is widely used in various scenarios. However, when the data contains sensitive information, it will bring serious privacy leakage risks to mining and publishing it directly. Therefore, how to efficiently mine frequent itemset without privacy disclosure is a hot issue at present. In data stream, because the data between adjacent timestamps possess certain correlation, which makes it easier to leak privacy for frequent itemset mining in the data stream, and considering that frequent itemset will have combinatorial explosion problem in data stream, frequent closed itemset mining in the data stream will be a better choice. In this paper, we propose a differential privacy algorithm for mining frequent closed itemset in data stream, which is referred to as DPES. The algorithm uses a vertical mining method in each sliding window, which can quickly obtain the frequent closed itemset contained in the current window. Besides, to improve the data utility, we also design an adaptive privacy budget allocation strategy by computing the difference to decide whether the current timestamp should publish a low-noise statistic or an approximate statistic. Finally, we demonstrate that the DPES algorithm satisfies ε-difference privacy by privacy analysis, and the experimental results on several real datasets also show the effectiveness of the DPES algorithm.

关键词： Differential privacy Privacy Correlation Itemsets Publishing Noise Approximation algorithms

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FaaSTCC: efficient transactional causal consistency for serverless computing 21

FaaSTCC: efficient transactional causal consistency for serv...

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proceedings of the 22nd international Middleware conference

作者： Taras Lykhenko Rafael Soares Luis Rodrigues Universidade de Lisboa Portugal

ISBN: (纸本)9781450385343

In this paper we study mechanisms that permit to augment the FaaS middleware with support for Transactional Causal Consistency (TCC). At first glance, it may seem that offering TCC to FaaS applications can trivially be achieved, given that the FaaS paadigm does not prevent applications from selecting the storage service with the properties they need. Unfortunately, most TCC storage services ensure consistency only to individual client processes, while a FaaS application is executed by multiple, independent, worker processes. Therefore, there is the need to coordinate the workers, a task that can be a significant source of overhead. We propose a novel architecture to support TCC in FaaS, named FaaSTCC, that significantly reduces the coordination overhead. FaaSTCC achieves this goal by augmenting the workers with a caching layer and by implementing novel mechanisms that maximize the cache usage. First, our storage layer offers to the caching layer a promise, that sets a horizon where the versions retrieved by the cache are guaranteed to be consistent. Second, in FaaSTCC, functions coordinate using snapshot intervals, that support the lazy identification of the read snapshot, increasing the chances of using the cached values. We have implemented and experimentally evaluated FaaSTCC. Our results show that FaaSTCC achieves up to 5x lower average latency and 6x lower tail latency than previous work.

关键词： function as a service distributed systems transactional causal consistency

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Evaluation of distributed Collaborative Learning Approach for 5G Network Data Analytics Function

Evaluation of Distributed Collaborative Learning Approach fo...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Shoufeng Wang Hua-Min Chen Ye Ouyang Fan Li Xuan Chen Limeng Ma Zhanwu Li Sen Bian Zhidong Ren Asiainfo Technologies (China) INC. China Faculty of Information Technology Beijing University of Technology Beijing China Network Optimization Center China Unicom Beijing Branch Beijing China China Unicom Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

As a key role in the future network functionalities, artificial intelligence (AI) has been studied widely. To match the envisioned traffic evolution trend and communication requirement, Network Data Analytic Function(NWDAF) is a new proposed 5G component to provide analytics for any Network Functions (NFs). Considering sending all data to a central NWDAF instance is extremely time-consuming and it raises concerns about security vulnerabilities and data overload, it's expected to design a distributed NWDAF architecture which is able to enhance NF data localization, improve security, reduce control overhead during model training, shorten the training time, and finally, enhance the accuracy of the trained models by virtue of local testing on a real-time network. Federated learning is considered in future NWDAF design, while its detailed structure and processes are not standardized yet. This paper studies the effectiveness of distributed federated learning based traffic characteristics analysis for NWDAF. Simulation shows that the learning accuracy is nolinear with data loss probability. Under the simulation assumption, the solution could endure 30% data loss, while the learning accuracy and the retransmission dramatically rise when data loss probability is greater than 45%.

关键词： Training Analytical models Data analysis Federated learning 5G mobile communication distributed databases Data models

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