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Multi-party privacy-preserving decision tree training with a privileged party

science China(Information sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party

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Online demand peak shaving with machine-learned advice in digital twins

Digital Twins and Applications

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Digital Twins and Applications 2024年第1期1卷 38-50页

作者： Minxi Feng Wei Li Boyu Qin Albert Y.Zomaya Centre for Distributed and High Performance Computing School of Computer ScienceThe University of SydneySydneyNew South WalesAustralia State Key Laboratory of Electrical Insulation and Power Equipment Department of Electrical EngineeringXi'an Jiaotong UniversityXi'anChina

As the use of physical instruments grows,control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin *** load management is central to energy systems within digital twins,which significantly impacts operational *** demand loads can lead to substantial monthly utility expenses without proper ***,a randomised online algorithm incorporating machine-learned insights is introduced to optimise battery operations and mitigate peak demand *** leverages limited-bit information from ma-chine learning models to inform its online decision-making process for cost-effective load *** provide theoretical evidence demonstrating that AMPAMOD maintains minimal advice complexity,has a linear computational cost,and achieves a bounded competitive *** trace-driven experiments with real-world household data reveal that AMPAMOD successfully reduces peak loads by over 90%,outperforming other benchmarks by at least 50%.These experimental findings align with our theoretical assertions,showcasing the effectiveness of AMPAMOD.

关键词： operations maintain battery

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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International Conference on Data Engineering

作者： Junhui Gao Qianru Wang Xin Zhang Juan Shi Xiang Zhao Qingye Han Yan Pan School of Computer Science Northwestern Polytechnical University School of Computer Science and Technology Xidian University National Key Laboratory of Information Systems Engineering National University of Defense Technology Air Force Engineering University Laboratory for Big Data and Decision National University of Defense Technology School of Management Science and Real Estate Chongqing University National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery.

关键词： Autonomous aerial vehicles Data engineering Air to ground communication Trajectory Batteries Time factors Task analysis

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CGS-Mask: Making Time Series Predictions Intuitive for All

arXiv

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arXiv 2023年

作者： Lu, Feng Li, Wei Sun, Yifei Song, Cheng Ren, Yufei Zomaya, Albert Y. School of Computer Science and Technology Huazhong University of Science and Technology China The Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a single time point, overlook the time ordering of inputs, and neglect the time-sensitive nature of time series applications. These factors make it difficult for users, particularly those without domain knowledge, to comprehend AI model decisions and obtain meaningful explanations. We propose CGS-Mask, a post-hoc and model-agnostic cellular genetic strip mask-based saliency approach to address these challenges. CGS-Mask uses consecutive time steps as a cohesive entity to evaluate the impact of features on the final prediction, providing binary and sustained feature importance scores over time. Our algorithm optimizes the mask population iteratively to obtain the optimal mask in a reasonable time. We evaluated CGS-Mask on synthetic and real-world datasets, and it outperformed state-of-the-art methods in elucidating the importance of features over time. According to our pilot user study via a questionnaire survey, CGS-Mask is the most effective approach in presenting easily understandable time series prediction results, enabling users to comprehend the decision-making process of AI models with ease. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Online Demand Peak Shaving with Machine-Learned Advice in Cyber-Physical Energy Systems

Online Demand Peak Shaving with Machine-Learned Advice in Cy...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Minxi Fengl Wei Li Boyu Qin Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Electrical Engineering State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University China

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD.

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Deep Isolation Forest for Anomaly Detection

arXiv

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arXiv 2022年

作者： Xu, Hongzuo Pang, Guansong Wang, Yijie Wang, Yongjun The College of Computer National University of Defense Technology Changsha410073 China The Science and Technology on Parallel and Distributed Processing Laboratory China The School of Computing and Information Systems Singapore Management University 178902 Singapore

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation forest. We introduce a new representation scheme that utilises casually initialised neural networks to map original data into random representation ensembles, where random axis-parallel cuts are subsequently applied to perform the data partition. This representation scheme facilitates high freedom of the partition in the original data space (equivalent to non-linear partition on subspaces of varying sizes), encouraging a unique synergy between random representations and random partition-based isolation. Extensive experiments show that our model achieves significant improvement over state-of-the-art isolation-based methods and deep detectors on tabular, graph and time series datasets;our model also inherits desired scalability from iForest. © 2022, CC BY.

关键词： Anomaly detection

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distributed Online Resource Scheduling for Mobile Edge Servers

Distributed Online Resource Scheduling for Mobile Edge Serve...

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IEEE International Conference on Edge computing (EDGE)

作者： Ziwen Zhou Tianming Zhao Wei Li Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia

ISBN: (纸本)9781665400633

Due to the flexibility and scalability, the increasing number of mobile edge computing applications involves Mobile Edge Servers (MES). MES introduces the new challenge of online resource scheduling to serve different requests under limited energy to offer similar functionalities of the immobile edge servers. The previous studies consider the case of the identical server, which has poor scalability and is hard to apply to real-world applications. This work proposes a novel model named the distributed k-server problem that formulates the MES resource scheduling to address the heterogeneity issues in both servers and requests. We design an algorithm named DWFA based on the efficient network flow-based Work Function Algorithm (WFA) to the classic k-server problem as an immediate solution to the proposed problem. DWFA inher-its the competitiveness of WFA but has high computational complexity. To further increase scalability via the computing power of MES, we parallelise DWFA to design a distributed algorithm named FD-WFA as a distributed execution of DWFA, which significantly reduces the computational complexity and increases the practicality. Extensive simulations have been con-ducted to verify the theoretical results and show the advantages of FD-WFA over the benchmarks.

关键词： Multi-access edge computing Processor scheduling Power supplies Scalability Computational modeling Benchmark testing Servers

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LTKT: Knowledge Tracing Based on Positive and Negative Learning Transfers

SSRN

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SSRN 2023年

作者： Xu, Jia Tang, Rongrong Lv, Pin Yu, Minghe Yu, Ge Chen, Enhong School of Computer Electronics and Information Guangxi University GuangXi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi530004 China Northeastern University Shenyang110819 China University of Science and Technology of China Hefei230088 China

Knowledge Tracing (KT) is a critical but challenging problem for many educational applications. As an essential part of educational psychology, the propagated influence among pedagogical concepts (i.e., learning transfer) is important for optimizing KT tasks. However, existing KT methods only consider the positive learning transfer and neglect the negative learning transfer. To this end, in this paper, we propose a novel deep knowledge tracing model, called positive and negative Learning Transfers based Knowledge Tracing (LTKT). To the best of our knowledge, LTKT makes the first attempt to concurrently utilize the positive and negative learning transfer relations among concepts to improve KT results. First, LTKT employs a statistical-based approach to construct a learning transfer graph (LTG). Then, LTKT quantifies the impact of an exercise's practice result on the knowledge state of the concept via a direct learning effect component, after which a learning transfer effect component is carefully designed to quantify the impact of the practice result on the knowledge states of neighboring concepts based on the positive and negative learning transfer relations modeled by LTG. We conduct extensive experiments on public real-world datasets, and the experimental results show that LTKT outperforms all state-of-the-art KT methods and has good interpretability. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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A Case Study of Developing an Intelligent Management System for Energy Internet 4

A Case Study of Developing an Intelligent Management System ...

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4th IEEE International Conference on Energy Internet, ICEI 2020

作者： Chi, Lian Zhao, Zezheng Xia, Chunqiu Chang, Xiaomin Nanjing University of Information Science and Technology Business School China University of Sydney School of Computer Science Centre for Distributed and High Performance Computing Australia

ISBN: (纸本)9780738105000

Energy Internet (EI) has drawn increasingly interests in related industries. The implement of EI is a milestone on the road-map to promote the revolution of both energy production and consumption, which is important to achieve sustainable energy development. This paper investigates the possibility on establishing the EI by integrating application-level intelligent management system with traditional energy grid in neighbourhood scale. As a new energy utilisation model, EI realises the deep integration between energy industry and information and communications technology (ICT) in order to optimise regional energy management and optimal dispatch. With the support of Internet of things technology and big data technology, this system plays an important role in coordinating management and scheduling while realising the interaction between market entities and end-users at the same time. This paper reviews and conducts in-depth research on the background, concept and technical model of EI, and also discusses the implementation of EI in a practical industrial parks. © 2020 IEEE.

关键词： Internet of Energy

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