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A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

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Optimizing accuracy and diversity: a multi-task approach to forecast combinations

arXiv

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arXiv 2023年

作者： Felici, Giovanni Sudoso, Antonio M. Institute for Systems Analysis and Computer Science Antonio Ruberti National Research Council Via dei Taurini 19 Rome00185 Italy Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Via Ariosto 25 Rome00185 Italy

Forecast combination involves using multiple forecasts to create a single, more accurate prediction. Recently, feature-based forecasting has been employed to either select the most appropriate forecasting models or to optimize the weights of their combination. In this paper, we present a multi-task optimization paradigm that focuses on solving both problems simultaneously and enriches current operational research approaches to forecasting. In essence, it incorporates an additional learning and optimization task into the standard feature-based forecasting approach, focusing on the identification of an optimal set of forecasting methods. During the training phase, an optimization model with linear constraints and quadratic objective function is employed to identify accurate and diverse methods for each time series. Moreover, within the training phase, a neural network is used to learn the behavior of that optimization model. Once training is completed the candidate set of methods is identified using the network. The proposed approach elicits the essential role of diversity in feature-based forecasting and highlights the interplay between model combination and model selection when optimizing forecasting ensembles. Experimental results on a large set of series from the M4 competition dataset show that our proposal enhances point forecast accuracy compared to state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Convolutional Neural Network Optimization Using Modified NSGA-II 11

Convolutional Neural Network Optimization Using Modified NSG...

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11th IEEE Annual International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2021

作者： Su, Zhidong Sheng, Weihua Zhang, Senlin School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665425278

Convolutional Neural Networks (CNN) are becoming deeper and deeper. It is challenging to deploy the networks directly to embedded devices because they may have different computational capacities. When deploying CNNs, the trade-off between the two objectives: accuracy and inference speed, should be considered. NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm is a multi-objective optimization algorithm with good performance. The network architecture has a significant influence on the accuracy and inference time. In this paper, we proposed a convolutional neural network optimization method using a modified NSGA-II algorithm to optimize the network architecture. The NSGA-II algorithm is employed to generate the Pareto front set for a specific convolutional neural network, which can be utilized as a guideline for the deployment of the network in embedded devices. The modified NSGA-II algorithm can help speed up the training process. The experimental results show that the modified NSGA-II algorithm can achieve similar results as the original NSGA-II algorithm with respect to our specific task and saves 46.20% of the original training time. © 2021 IEEE.

关键词： Convolutional neural networks

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Cache Replacement Strategy of Cooperative Edge Caching Based on Discrete Maddpg

SSRN

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SSRN 2023年

作者： Wei, Zhenchun Fu, Yu Yuan, Xiaohui Lyu, Zengwei Zhang, Yu Shi, Lei School of Computer Science and Information Engineering Hefei University of Technology 193 Tunxi Road Anhui Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology 193 Tunxi Road Anhui Hefei230009 China Department of Computer Science and Engineering University of North Texas 3940 N. Elm DentonTX76203 United States

Cooperative edge caching has emerged as a promising solution to alleviate the traffic burden of backhaul and improve the Quality of Service (QoS) of 5G applications in the 5G era. Several cooperative edge caching methods alleviate the traffic burden of backhaul by transmitting contents between edge nodes cooperatively, thereby reducing the delay of content transmission. However, they only consider the cooperation on content transmission and do not consider the cooperation on cache replacement, which can not control content redundancy in the cooperative edge caching system and reduce the utilization of cache space. In this paper, we propose the cache replacement strategy of cooperative edge caching based on the Discrete Multi-Agent Deep Deterministic Policy Gradient (Discrete MADDPG) algorithm. We first propose a cooperative edge caching model to maximize the system throughput. Then we formulate the cache replacement process of the cooperative edge caching system to a Markov Game (MG) model. Finally, we use the cache replacement strategy based on the Discrete MADDPG algorithm to solve the MG model and control content redundancy. The simulation results show that the proposed algorithm achieves a higher system throughput and can control content redundancy. © 2023, The Authors. All rights reserved.

关键词： Redundancy

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Photovoltaic power forecasting based on weather classification using LSTM

Photovoltaic power forecasting based on weather classificati...

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第32届中国过程控制会议(CPCC2021)

作者： Lei Guo Xiangjie Liu Xiaobing Kong School of Control and Computer Engineering North China Electric Power University

In order to reduce possible negative impacts of photovoltaic(PV) power penetration into modern power grids,an accurate forecasting of PV power output is ***,photovoltaic power forecasting(PVPF) is a rather difficult challenge because of its highly varying and complex *** this article,a weather-based hybrid PVPF method is proposed,combining weather classification,long short-term memory network,and the fuzzy inference approach to make 5-minutes-ahead forecasting of PV power *** correlation coefficient is adopted as a feature selection *** effectiveness of the proposed model is evaluated by comparing with the multilayer perceptron neural network and traditional long short-term memory *** results show that the proposed model shows better forecasting accuracy.

关键词： Photovoltaic power forecasting Weather classification Long short-term memory networks Fuzzy inference approach Pearson correlation coefficient

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Modelling of SFR for Wind-Thermal Power Systems via Improved RBF Neural Networks

Modelling of SFR for Wind-Thermal Power Systems via Improved...

引用

Chinese Intelligent Systems Conference, CISC 2020

作者： Zhang, Jianhua Li, Hongrui Hu, Bo Min, Yong Chen, Qun Hou, Guolian Huang, Congzhi State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China School of Control and Computer Engineering Tsinghua University Beijing100089 China

ISBN: (纸本)9789811584572

In this paper, one kind of system identification algorithm based on an improved radial basis function (RBF) neural networks is presented for nonlinear and non-Gaussian systems. Survival information potential (SIP) of identification errors is employed to constructed the performance index to train the RBF neural networks. The data driven system identification algorithm implemented by the proposed RBF neural networks is applied to obtain the equivalent model of wind-thermal integrated power systems. Compared with the traditional RBF neural networks and back-propagation (BP) neural networks based on mean square error (MSE) criteria, simulation results demonstrate that the proposed system identification algorithm can obtain better SFR models for wind-thermal integrated power systems. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Functions

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Fault Modeling of Electro Hydraulic Actuator in Gas Turbine control System Based on Matlab/Simulink

Fault Modeling of Electro Hydraulic Actuator in Gas Turbine ...

引用

第33届中国控制与决策会议

作者： Yanquan Liu Lele Tian Zhaoya Li Wenguang Zhang School of Control and Computer Engineering North China Electric Power University

As a practical tool,gas turbine will inevitably be affected by various factors in normal *** the most important part of gas turbine control system,actuator has higher requirements for fault diagnosis ***,the research on fault diagnosis of hydraulic actuator of gas turbine control system is relatively lacking at home and *** paper is a fault model of electro-hydraulic actuator of gas turbine control *** of all,through the working principle of the electro-hydraulic actuator mechanism modeling,and then compare and analyze the model to establish the electro-hydraulic actuator fault diagnosis model,Finally,in the MATLAB/Simulink platform to simulate the approximation of the mathematical model,get the dynamic response results.

关键词： Gas Turbine control System Actuator Fault Diagnosis MATLAB/Simulink Mechanism Modeling

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Supporting Impaired People with a Following Robotic Assistant by means of End-to-End Visual Target Navigation and Reinforcement Learning Approaches

Supporting Impaired People with a Following Robotic Assistan...

引用

2021 International Conference of Yearly Reports on Informatics, Mathematics and engineering, ICYRIME 2021

作者： Dat, Nguyen Ngoc Ponzi, Valerio Russo, Samuele Vincelli, Francesco Department of Computer Control and Management Engineering Sapienza Univerisity of Rome Rome00185 Italy Department of Psychology Sapienza University of Rome via dei Marsi 78 Roma00185 Italy

We present an improvement in visual object tracking and navigation for mobile robot implementing the advantage actor-critic (A2C) reinforcement learning architecture on top of the Gym-Gazebo framework. This work provides an easier way to integrate reinforcement learning algorithms for navigation and object tracking tasks in robotics field. We train the convolutional-recurrent model employed for the policy estimation in an end-to-end manner. The robot is able to follow a simulated human walking in an indoor environment by using the sequence of images provided by the robot camera. The input of the algorithm is acquired and processed directly in ROS-Gazebo environment. The policy learned by the robot agent proved to generalize well also in an environment with different size and shape with respect to the training one. Moreover, the policy allows the robot to avoid obstacles while following the tracking target. Thanks to these improvements, we can straightforwardly apply the tracking system in a real world robot for a person following task in indoor environments. © 2021 Copyright for this paper by its authors

关键词： Reinforcement learning

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Stock price prediction model based on RBF-SVM algorithm

Stock price prediction model based on RBF-SVM algorithm

引用

2020 International Conference on computer engineering and Intelligent control, ICCEIC 2020

作者： Liu, Zixuan Dang, Ziyuan Yu, Jie North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9781728185736

At this stage, China's economic development continues to progress, and the emergence of various emerging industries has caused the stock market to show strong volatility. However, people's research and prediction on the stock market have never stopped. In order to improve the accuracy of stock prediction, this paper studies the network model based on the improved support vector machine (SVM) algorithm to realize the correct judgment of the stock price trend, so as to achieve the purpose of accurate stock price prediction, and improve the accuracy of the prediction while ensuring the speed of the model. Experiments show that the proposed prediction model can approximate the short-term price trend of the stock market, and provide a more reliable data basis for the accurate prediction of stock prices, which benefit the high-tech development and progress of the stock market. © 2020 IEEE.

关键词： Support vector machines

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Explaining the deep natural language processing by mining textual interpretable features

arXiv

引用

arXiv 2021年

作者： Ventura, Francesco Greco, Salvatore Apiletti, Daniele Cerquitelli, Tania Department of Control and Computer Engineering Politecnico di Torino Turin Italy

Despite the high accuracy offered by state-of-the-art deep natural-language models (e.g. LSTM, BERT), their application in real-life settings is still widely limited, as they behave like a black-box to the end-user. Hence, explainability is rapidly becoming a fundamental requirement of future-generation data-driven systems based on deep-learning approaches. Several attempts to fulfill the existing gap between accuracy and interpretability have been done. However, robust and specialized xAI (Explainable Artificial Intelligence) solutions tailored to deep natural-language models are still missing. We propose a new framework, named T-EBAnO, which provides innovative prediction-local and class-based model-global explanation strategies tailored to black-box deep natural-language models. Given a deep NLP model and the textual input data, T-EBAnO provides an objective, human-readable, domain-specific assessment of the reasons behind the automatic decision-making process. Specifically, the framework extracts sets of interpretable features mining the inner knowledge of the model. Then, it quantifies the influence of each feature during the prediction process by exploiting the novel normalized Perturbation Influence Relation index at the local level and the novel Global Absolute Influence and Global Relative Influence indexes at the global level. The effectiveness and the quality of the local and global explanations obtained with T-EBAnO are proved on (i) a sentiment analysis task performed by a fine-tuned BERT model, and (ii) a toxic comment classification task performed by an LSTM model. © 2021, CC BY.

关键词： Long short-term memory

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