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Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

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Digital Communications and networks 2024年第2期10卷 380-388页

作者： Renwan Bi Mingfeng Zhao Zuobin Ying Youliang Tian Jinbo Xiong Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China Faculty of Data Science City University of Macao999078MacaoChina State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in ***,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy *** solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement ***,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also ***,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy ***,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

关键词： Mobile edge crowdsensing Dynamic privacy measurement Personalized privacy threshold Privacy protection Reinforcement learning

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Privacy-preserving Trajectory data Release Hierarchical Model 13

Privacy-preserving Trajectory Data Release Hierarchical Mode...

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13th International Conference on Communication and network security, ICCNS 2023

作者： Zhang, Qian Zhang, Xing Shi, Wei Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Liaoning University of Technology Jinzhou121000 China

ISBN: (纸本)9798400707964

The development of positioning technology and mobile intelligent devices has given rise to plentiful location-based services and generated much trajectory data. The publication and analysis of trajectory data bring convenience to users, but also carry the risk of sensitive information being exposed. Existing trajectory data publication models mostly only consider the processing of static trajectory data and do not effectively combine spatiotemporal properties, and the problem of dishonest third parties unconsciously collecting data from other parties also occurs from time to time. This paper proposes a new privacy-preserving trajectory data publication hierarchical model based on secure two-party computation. The user layer, the cloud layer, and the local server layer collaborate to complete the trajectory publishing process. Experimental statistics have proved that the proposed method has better privacy and data availability. © 2023 ACM.

关键词： Trajectories

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UVHM: Model checking based formal analysis scheme for hypervisors

UVHM: Model checking based formal analysis scheme for hyperv...

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International Conference on Information and Communication Technology, ICT-EurAsia 2013

作者： She, Yuchao Li, Hui Zhu, Hui Xidian University Xi'an 710071 China Network and Data Security Key Laboratory of Sichuan Province Chengdu 611731 China

ISBN: (纸本)9783642368172

Hypervisors act a central role in virtualization for cloud computing. However, current security solutions, such as installing IDS model on hypervisors to detect known and unknown attacks, can not be applied well to the virtualized environments. Whats more, people have not raised enough concern about vulnerabilities of hypervisors themselves. Existing works mainly focusing on hypervisors' code analysis can only verify the correctness, rather than security, or only be suitable for open-source hypervisors. In this paper, we design a binary analysis tool using formal methods to discover vulnerabilities of hypervisors. In the scheme, Z notation, VDM, B, Object-Z or CSP formalism can be utilized as suitable modeling and specification languages. Our proposal sequently follows the process of disassembly, modeling, specification, and verification. Finally, the effectiveness of the method is demonstrated by detecting the vulnerability of Xen-3.3.0 in which a bug is added. © 2013 Springer-Verlag.

关键词： Model checking

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A Performance Comparison Study of Parallel Clustering Algorithms in Cluster Environments 2

A Performance Comparison Study of Parallel Clustering Algori...

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2017 IEEE 2nd International Conference on Big data Analysis（ICBDA 2017）

作者： Mo Hai School of Information Central University of Finance and Economics Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China

ISBN: (纸本)9781509036202

We compare the performance of three parallel clustering algorithms:Canopy,K-means and fuzzy K-means in real cluster *** constructing cluster platform of different scale,we compare these algorithms from three metrics:run time,speedup and *** results show that:(1) if both the data set and the number of nodes in the cluster are the same,both the runtime and the sizeup of Canopy is the smallest,and the speedup of Canopy is the largest among these three algorithms;(2) when the number of nodes is larger than 2,for each algorithm,the difference between the sizeup on different data set becomes more and more smaller as the number of nodes increases;at the same time,when the number of nodes is larger than 2 and the data set is the same,the sizeup of Canopy has the maximum decrease with the increase of the number of nodes;(3) when the number of nodes keeps the same,the difference between the sizeup of these three algorithms becomes more and more apparent as the size of the data set increases.

关键词： Canopy K-means Fuzzy K-means Cluster platform Run time Speedup Sizeup

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Provably secure and subliminal-free variant of Schnorr signature

Provably secure and subliminal-free variant of Schnorr signa...

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International Conference on Information and Communication Technology, ICT-EurAsia 2013

作者： Zhang, Yinghui Li, Hui Li, Xiaoqing Zhu, Hui Xidian University Xi'an 710071 China Network and Data Security Key Laboratory of Sichuan Province Chengdu 611731 China

ISBN: (纸本)9783642368172

Subliminal channels present a severe challenge to information security. Currently, subliminal channels still exist in Schnorr signature. In this paper, we propose a subliminal-free variant of Schnorr signature. In the proposed scheme, an honest-but-curious warden is introduced to help the signer to generate a signature on a given message, but it is disallowed to sign messages independently. Hence, the signing rights of the signer is guaranteed. In particular, our scheme can completely close the subliminal channels existing in the random session keys of Schnorr signature scheme under the intractability assumption of the discrete logarithm problem. Also, the proposed scheme is proved to be existentially unforgeable under the computational Diffie-Hellman assumption in the random oracle model. © 2013 Springer-Verlag.

关键词： Electronic document identification systems

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IoT-Driven Predictive Analytics for Precision Harvest Timing with LightGBM Models

IoT-Driven Predictive Analytics for Precision Harvest Timing...

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Visual Analytics and data Visualization (ICVADV), International Conference on

作者： S. Lourdu Jame Chitra Sabapathy Ranganathan S.K. Saravanan Bharat Tidke E. Punarselvam M. Rajmohan Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Mphasis Corporation Chennai Tamil Nadu India Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Pune India Department of Information Technology Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In agriculture, precise harvest time is essential to maximize production and quality. A predictive analytics system powered by the Internet of Things (IoT) and using LightGBM models for accurate harvest time determination is presented in this study. The framework incorporates IoT sensors to gather environmental data, such as soil moisture, light intensity, temperature, and humidity, which are important markers of crop maturity. The data is analyzed using machine learning models, namely LightGBM, to estimate when to harvest accurately. The proposed framework seeks to improve agricultural efficiency by using real-time data analytics. This would allow farmers to make better crop output, waste, and resource utilization choices. Farmers may dynamically adjust their harvesting plans in response to changing crop conditions due to the IoT-driven strategy that enables constant monitoring of environmental variables. By reducing input waste and environmental effects, this capacity enhances agricultural operations' total productivity while supporting sustainable farming practices. The proposed model can transform harvest management using advanced predictive analytics and IoT technologies, as shown by case studies and simulations confirming its efficacy in several agricultural settings.

关键词： Temperature sensors Analytical models Accuracy Humidity Predictive models Agriculture Real-time systems data models Sensors Predictive analytics

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Fault Diagnosis for Rolling Bearing Based on RF-LSTM

Fault Diagnosis for Rolling Bearing Based on RF-LSTM

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Li, Qiuting Wang, Xiuqing Yang, Yunpeng Wang, Ruiyi Lv, Feng Hebei Normal University College of Computer and Cyber Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China

ISBN: (纸本)9781665401159

In the era of big data, it is of great significance to study data-driven fault diagnostic methods for equipment in complex industrial environments. In this paper, a data-driven fault diagnostic method for rolling bearing based on RF-LSTM is proposed, which combines random forest (RF) algorithm with long and short term memory (LSTM) networks. RF is used to select sensor data's features, which have great influence on rolling bearing fault diagnosis, and LSTM is used for prediction rolling bearing's working states. The proposed method is used for fault diagnosis of the Case Western Reserve University (CWRU) bearing dataset and the experimental results validated the effectiveness of the proposed RF-LSTM model. Experimental results also show that the performance of the RFLSTM model is better than that of RF, LSTM, RF-RF, PCA-RF, PCA-LSTM models. © 2021 IEEE.

关键词： Failure analysis

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Multi-scale hierarchical cross fusion network for hyperspectral image and LiDAR classification

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Journal of the Franklin Institute 2025年第9期362卷

作者： Pan, Haizhu Li, Xuehu Ge, Haimiao Wang, Liguo Yu, Xiaomin College of Computer and Control Engineering Qiqihar University Qiqihar161000 China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University Qiqihar161000 China College of Information and Communication Engineering Dalian Nationalities University Dalian116000 China

As the satellite sensor technology continues to advance, acquiring hyperspectral images (HSI) and Light Detection and Ranging (LiDAR) data have become increasingly accessible. Consequently, the fusion of these two types of data has become very popular in land cover classification tasks. However, there are still some deficiencies in the current methods, such as insufficient extraction of distinctive features of multi-source data, and inadequate fusion caused by poor use of the complementarity and cooperation in multi-source data. Therefore, we propose a multi-scale hierarchical cross fusion network (MSHCFNet) for the fusion classification of HSI and LiDAR. It includes four parts. Firstly, the multi-scale cascading feature extraction module is used to extract fully multi-scale spatial features. Secondly, the hierarchical fusion module is used to integrate spatial features by cross fusion and group fusion. Thirdly, the spectral signature enhancement module is used to enhance the spectral signature, so that the discriminant information in the spectral signature can be used effectively. Finally, the decision fusion module is used to classify the types of ground objects. A large number of experiments on three publicly available datasets show that the proposed method has the most advanced classification efficacy compared with existing methods. © 2025

关键词： Feature Selection

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Overcoming the obstacles of zero-knowledge watermark detection

Overcoming the obstacles of zero-knowledge watermark detecti...

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Proceedings of the Multimedia and security Workshop 2004, MM and Sec'04

作者： Adelsbach, André Rohe, Markus Sadeghi, Ahmad-Reza Dept. of Network- and Data-Security Ruhr-Universität Bochum Bochum Germany Universität Saarbrücken Saarbrücken Germany Department of Applied Data Security Ruhr-Univeristät Bochum Bochum Germany

ISBN: (纸本)1581138547

Standard watermarking schemes suffer from a major problem: They require to reveal security critical information to potentially untrusted parties, when proving the presence of a watermark to these parties. Zero-knowledge watermark detection is a promising means to overcome this problem and to improve the security of digital watermarking schemes in the context of various applications: it allows to cryptographically conceal the information required for the detection of a watermark and to prove the presence of the hidden watermark by efficient zero-knowledge proof systems. However, concealing the watermark prevents the verifying party from performing additional checks on the watermark, e.g., on its probability distribution, which may be required for certain applications. This is a limitation in the use of zero-knowledge watermark detection and we present several strategies to overcome this issue. Furthermore, we propose concrete and practical protocols, which pursue two promising strategies: the first strategy is to prove in zero-knowledge that a concealed watermark suffices a certain desired distribution, whereas the second strategy is to interactively and verifiably generate watermarks that suffice the desired distribution. Copyright 2004 ACM.

关键词： Digital watermarking

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