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arXiv 2024年

作者： Park, Hyun-Young Asoodeh, Shahab Lee, Si-Hyeon School of Electrical Engineering KAIST Korea Republic of Department of Computing and Software McMaster University Canada

The sampling problem under local differential privacy has recently been studied with potential applications to generative models, but a fundamental analysis of its privacy-utility trade-off (PUT) remains incomplete. In this work, we define the fundamental PUT of private sampling in the minimax sense, using the f-divergence between original and sampling distributions as the utility measure. We characterize the exact PUT for both finite and continuous data spaces under some mild conditions on the data distributions, and propose sampling mechanisms that are universally optimal for all f-divergences. Our numerical experiments demonstrate the superiority of our mechanisms over baselines, in terms of theoretical utilities for finite data space and of empirical utilities for continuous data space. © 2024, CC BY-NC-SA.

关键词： Differential privacy

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Towards Dynamic Evolutionary Analysis of ProgrammableWeb for API-Mashup Ecosystem 26

Towards Dynamic Evolutionary Analysis of ProgrammableWeb for...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Yang Xiang, Jiayan Cheng, Hangyu Chen, Wanjun Xiao, Yong Kang, Guosheng Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hnust Xiangtan China

ISBN: (纸本)9798350331684

With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added application. With the increasing number of Web APIs and Mashups, the API-Mashup ecosystem has emerged based on the invocation relationship between Mashups and Web APIs. In this paper, we take ProgrammableWeb, a typical API-Mashup ecosystem, as an example to investigate its dynamic evolutionary analysis. Although there have been some works on the API-Mashup ecosystem, they mainly focus on static analysis, i.e., the static characteristics of the API- Mashup ecosystem on a fixed time point. This paper conducts a comprehensive study on the dynamic evolutionary analysis of the API-Mashup ecosystem with a long time range from 2005 to 2021. First, we conduct a dynamic statistical analysis based on the API-Mashup ecosystem dataset. Next, we construct two cooperation networks, one between Web APIs, and the other between their categories. And the general characteristics of the two cooperation networks are presented. Finally, we investigate the derived cooperation networks from four perspectives: dynamic characteristics, degree distribution, betweenness centrality, and assortative mixing. Meanwhile, the corresponding insights are uncovered. Our work provides a foundation for visualization and understanding of the API-Mashup ecosystem from the timeline. © 2023 IEEE.

关键词： Ecosystems

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LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based Adaptive Live Streaming 36

LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based A...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Tashtarian, Farzad Bentaleb, Abdelhak Amirpour, Hadi Taraghi, Babak Timmerer, Christian Hellwagner, Hermann Zimmermann, Roger Alpen-Adria-Universitat Klagenfurt Christian Doppler Laboratory Athena Austria Concordia University Department of Computer Science and Software Engineering Canada National University of Singapore School of Computing Singapore

ISBN: (纸本)9781665477161

Video content in Live HTTP Adaptive Streaming (HAS) is typically encoded using a pre-defined, fixed set of bitrate-resolution pairs (termed Bitrate Ladder), allowing play-back devices to adapt to changing network conditions using an adaptive bitrate (ABR) algorithm. However, using a fixed one-size-fits-all solution when faced with various content complexities, heterogeneous network conditions, viewer device resolutions and locations, does not result in an overall maximal viewer quality of experience (QoE). Here, we consider these factors and design LALISA, an efficient framework for dynamic bitrate ladder optimization in live HAS. LALISA dynamically changes a live video session's bitrate ladder, allowing improvements in viewer QoE and savings in encoding, storage, and bandwidth costs. LALISA is independent of ABR algorithms and codecs, and is deployed along the path between viewers and the origin server. In particular, it leverages the latest developments in video analytics to collect statistics from video players, content delivery networks and video encoders, to perform bitrate ladder tuning. We evaluate the performance of LALISA against existing solutions in various video streaming scenarios using a trace-driven testbed. Evaluation results demonstrate significant improvements in encoding computation (24.4%) and bandwidth (18.2%) costs with an acceptable QoE. © 2023 IEEE.

关键词： Bandwidth

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Offloading approach for mobile edge computing based on chaotic quantum particle swarm optimization strategy

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Journal of Ambient Intelligence and Humanized computing 2023年第10期14卷 14333-14347页

作者： Zhang, DeGan Sun, GuiXiang Zhang, Jie Zhang, Ting Yang, Peng Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China School of Sports Economics and Management Tianjin University of Sport Tianjin301617 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100093 China

With the development of 5 g, computing-intensive and complex applications in smart-city is growing rapidly. Due to the limited resources of mobile terminal devices in smart-city, new applications have higher requirements for delay, bandwidth, security, and energy consumption. Computation offloading in mobile edge computing (MEC) is effective to reduce delay and energy consumption of Real-time video analysis. An improved chaos quantum-behaved particle swarm optimization (ICQPSO) algorithm is proposed for multi-user and multi-MEC edge Computation offloading scenarios. Compared with other heuristic algorithms, the improved chaos quantum-behaved particle swarm optimization algorithm can effectively reduce the delay and energy consumption of edge computing offloading. Experimental results show that the improved chaotic quantum-behaved particle swarm optimization (ICQPSO) can effectively avoid premature convergence, has stronger global searchability, and can solve multi-dimensional complex NP-hard problems more efficiently. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Energy utilization

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer Science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Cybersecurity in Malware Research

Cybersecurity in Malware Research

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Horatiu Lupsan Remaz Ahmed Yong Shi College of Computing and Software Engineering Kennesaw State University Marietta USA

As most of our daily activities and routines are connected to technology and software, cybersecurity and malware research become significantly more important in ensuring that our personal information is kept private and secure. A significant increase of our interactive environments becoming digitalized has caused cybersecurity teams to play a constant catch up game to ensure the latest updates and platforms are secured against exploitation attempts from malicious entities. With modern technological advancements and discoveries being made, malware evolves, and attackers constantly find new ways to break into a system and exploit the vulnerabilities and shortcomings that are not addressed during the development process. In this paper we analyze four of the most recent malwares and discuss solutions to detect and prevent them from affecting our daily lives.

关键词： Conferences Games Malware Computer security

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Design and Implementation of an ERC-20 Smart Contract on the Ethereum Blockchain

Design and Implementation of an ERC-20 Smart Contract on the...

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IEEE International Conference on Big Data

作者： Joshua Priest Cameron Cooper Savvy Lovell Yong Shi Dan Lo College of Computing and Software Engineering Kennesaw State University Marietta USA

As technology continues to develop, there is a growing need to find sustainable solutions in all industries, including cryptocurrency. Due to the high energy consumption that cryptocurrencies are known for, there have been efforts to reduce waste consumption and in turn minimize the carbon footprint. We support the trends for creating an environment-friendly crypto token using the ERC-20 standard on the Ethereum blockchain. We outline the various aspects that make a token more sustainable and highlight the potential benefits of such tokens. Our proposal involves the design of a smart contract that incorporates eco-friendly features such as lower energy consumption, carbon offsetting, and more efficient methods or algorithms. We also discuss the importance of transparency and accountability in the design and implementation of such tokens. This paper discusses not only the practical tools and steps necessary in creating a crypto token but also highlights the challenges associated with creating a more sustainable token.

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Sensitive Information Protection Model Based on Bayesian Game

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Computers, Materials & Continua 2022年第10期73卷 883-898页

作者： Yuzhen Liu Zhe Liu Xiaoliang Wang Qing Yang Guocai Zuo Frank Jiang School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan411201China College of computer and information engineering Hunan University of Technology and BusinessChangshaChina School of Computer Science and Information Engineering Guangzhou Maritime UniversityGuangzhouChina Hunan Software Vocational and Technical University XiangtanChina School of Engineering and IT University of New South WalesNSWAustralia

A game measurement model considering the attacker’s knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of *** quantified the sensitive level of information according to the user’s personalized sensitive information protection *** on the probability distribution of sensitive level and attacker’s knowledge background type,the strategy combination of service provider and attacker was analyzed,and a game-based sensitive information protection model was *** the combination of strategies under Bayesian equilibrium,the information entropy was used to measure the leakage of sensitive ***,in the paper the influence of the sensitive level of information and the attacker’s knowledge background on the strategy of both sides of the game was considered *** on,the leakage of the user’s sensitive information was ***,the feasibility of the model was described by experiments.

关键词： Sensitive information game theory Bayesian equilibrium sensitive level information entropy

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3D Model Management and Dynamic Loading based on GIS Database

3D Model Management and Dynamic Loading based on GIS Databas...

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2022 International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2022

作者： Wang, Jiaxing Wang, Wei Yue, Yong Zhu, Xiaohui Xi'an Jiaotong-Liverpool University Department of Computing Suzhou China School of Software Engineering Hebei Normal University Shijiazhuang China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

This paper proposes a new method based Binary Large Object (BLO) to more effectively manage and load a massive number of three-dimensional (3D) models in the Geographic Information System (GIS). Different from those methods of 3D model storage and loading based on file systems, the proposed method focuses on integrating 3D model management and dynamic loading from a database. In the proposed method, the 3D model is split into several nodes and dynamically loaded with the GIS while the database keeps these nodes in a binary format. It aims to significantly improve the model loading efficiency. In order to verify the loading efficiency, a loading method of data stored in a database with the file system is introduced as a baseline. Typical examples show that the proposed BLO method is supreme over the baseline in terms of loading time for rendering 3D scenes, proving its high efficiency for loading large-scale scenes. © 2022 IEEE.

关键词： Efficiency

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A Privacy Preserving Computer-aided Medical Diagnosis Framework with Outsourced Model

A Privacy Preserving Computer-aided Medical Diagnosis Framew...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Feng, Xinyu Shen, Qingni Li, Cong Wang, Xuewei Xie, Niantao Xie, Luyuan Fang, Yuejian Wu, Zhonghai School of Software and Microelectronics Peking University Beijing102600 China School of Computer Science Peking University Beijing100871 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing100871 China

ISBN: (纸本)9798350337488

Computer-aided diagnosis plays an increasingly important role in modern medical activities, relying largely on the deployment of medical machine learning models. Protecting the security of model parameters is crucial for model providers. However, the current schemes for protecting model parameters are mostly interactive. This interactive nature makes it difficult to support offline deployment of models and flexible authorization of prediction results, thus hindering the widespread application of computer-aided diagnosis. To address these limitations, we propose a new computer-aided medical diagnosis framework by designing a new identity-based inner product functional proxy re-encryption (IB-IPFPRE) scheme. Our framework supports private deployment of medical diagnostic models without compromising model parameters. It also enables access control of prediction results based on user identity. Compared to existing privacy-preserving prediction techniques, our framework significantly reduces communication overhead and does not require the model owner to be online in real-time. Furthermore, our scheme enables flexible delegation of prediction results, allowing users to authorize the sharing of prediction results with other entities as needed. We conducted extensive experiments for logistic regression on three medical datasets. The experiments demonstrate that our scheme achieved 40% to 7× performance improvement in LAN environment and 13× to 15× improvement in WAN environment, and did not require any communication overhead during the privacy preserving prediction phase. © 2023 IEEE.

关键词： Computer-aided Diagnosis Identity-based Encryption Inner-product Functional Encryption Privacy Preserving Prediction Proxy Re-encryption

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