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

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 IoT.However,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 breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,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 designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,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.

Information Security Evaluation of Industrial Control Systems Using Probabilistic Linguistic MCDM Method

Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.

A Lightweight IoT Malware Detection and Family Classification Method

A lightweight malware detection and family classification system for the Internet of Things (IoT) was designed to solve the difficulty of deploying defense models caused by the limited computing and storage resources of IoT devices. By training complex models with IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Additionally, the multi-teacher knowledge distillation method is employed to train KD-LMDNet, which focuses on classifying malware families. The results indicate that the model’s identification speed surpasses that of traditional methods by 23.68%. Moreover, the accuracy achieved on the Malimg dataset for family classification is an impressive 99.07%. Furthermore, with a model size of only 0.45M, it appears to be well-suited for the IoT environment. By training complex models using IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Thus, the presented approach can address the challenges associated with malware detection and family classification in IoT devices.

An intelligent and privacy-enhanced data sharing strategy for blockchain-empowered Internet of Things

With the development of the Internet of Things(IoT),the massive data sharing between IoT devices improves the Quality of Service(QoS)and user experience in various IoT applications.However,data sharing may cause serious privacy leakages to data providers.To address this problem,in this study,data sharing is realized through model sharing,based on which a secure data sharing mechanism,called BP2P-FL,is proposed using peer-to-peer federated learning with the privacy protection of data providers.In addition,by introducing the blockchain to the data sharing,every training process is recorded to ensure that data providers offer high-quality data.For further privacy protection,the differential privacy technology is used to disturb the global data sharing model.The experimental results show that BP2P-FL has high accuracy and feasibility in the data sharing of various IoT applications.

Scalable Temporal Dimension Preserved Tensor Completion for Missing Traffic Data Imputation With Orthogonal Initialization

Dear Editor,This letter puts forward a novel scalable temporal dimension preserved tensor completion model based on orthogonal initialization for missing traffic data(MTD)imputation.The MTD imputation acts directly on accessing the traffic state,and affects the traffic management.

Conditional selection with CNN augmented transformer for multimodal affective analysis

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.

An Efficient and Privacy-Preserving Data Aggregation Scheme Supporting Arbitrary Statistical Functions in IoT

The Internet of Things(IoT)has profoundly impacted our lives and has greatly revolutionized our lifestyle.The terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent decisions.However,the high frequency of collecting user data will raise people concerns about personal privacy.In recent years,many privacy-preserving data aggregation schemes have been proposed.Unfortunately,most existing schemes cannot support either arbitrary aggregation functions,or dynamic user group management,or fault tolerance.In this paper,we propose an efficient and privacy-preserving data aggregation scheme.In the scheme,we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location sequence.On this basis,the proposed scheme supports not only arbitrary aggregation functions,but also flexible dynamic user management.Furthermore,the scheme achieves faulttolerant capabilities by utilizing a future data buffering mechanism.Security analysis reveals that the scheme can achieve the desired security properties,and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.

Achieving reliable and anti-collusive outsourcing computation and verification based on blockchain in 5G-enabled IoT

Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things(IoT)cloud to improve transmission efficiency,which has created a novel paradigm for improving the speed of common connected objects in IoT.However,although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services,security concerns,such as a lack of reliability and collusion attacks,still exist in the outsourcing computation.In this paper,we propose a reliable,anti-collusion outsourcing computation and verification protocol,which uses distributed storage solutions in response to the issue of centralized storage,leverages homomorphic encryption to deal with outsourcing computation and ensures data privacy.Moreover,we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum,which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion attacks.The results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure,reliable,and more effective compared with state-of-the-art approaches.

Linguistic Knowledge Representation in DPoS Consensus Scheme for Blockchain

The consensus scheme is an essential component in the real blockchain environment.The Delegated Proof of Stake(DPoS)is a competitive consensus scheme that can decrease energy costs,promote decentralization,and increase efficiency,respectively.However,how to study the knowledge representation of the collective voting information and then select delegates is a new open problem.To ensure the fairness and effectiveness of transactions in the blockchain,in this paper,we propose a novel fine-grained knowledge representation method,which improves the DPoS scheme based on the linguistic term set(LTS)and proportional hesitant fuzzy linguistic term set(PHFLTS).To this end,the symmetrical LTS is used in this study to express the fine-grained voting options that can be chosen to evaluate the blockchain nodes.PHFLTS is used to model the collective voting information on the voted blockchain nodes by aggregating the voting information from other blockchain nodes.To rank the blockchain nodes and then choose the delegate,a novel delegate selection algorithm is proposed based on the cumulative possibility degree.Finally,the numerical examples are used to demonstrate the implementation process of the proposed DPoS consensus algorithm and also its rationality.Moreover,the superiority of the proposed DPoS consensus algorithm is verified.The results show that the proposed DPoS consensus algorithm shows better performance than the existing DPoS consensus algorithms.

Machine Learning-Based Temperature and Wind Forecasts in the Zhangjiakou Competition Zone during the Beijing 2022 Winter Olympic Games

Weather forecasting for the Zhangjiakou competition zone of the Beijing 2022 Winter Olympic Games is a challenging task due to its complex terrain.Numerical weather prediction models generally perform poorly for cold air pools and winds over complex terrains,due to their low spatiotemporal resolution and limitations in the description of dynamics,thermodynamics,and microphysics in mountainous areas.This study proposes an ensemble-learning model,named ENSL,for surface temperature and wind forecasts at the venues of the Zhangjiakou competition zone,by integrating five individual models—linear regression,random forest,gradient boosting decision tree,support vector machine,and artificial neural network(ANN),with a ridge regression as meta model.The ENSL employs predictors from the high-resolution ECMWF model forecast(ECMWF-HRES) data and topography data,and targets from automatic weather station observations.Four categories of predictors(synoptic-pattern related fields,surface element fields,terrain,and temporal features) are fed into ENSL.The results demonstrate that ENSL achieves better performance and generalization than individual models.The root-mean-square error(RMSE) for the temperature and wind speed predictions is reduced by 48.2% and 28.5%,respectively,relative to ECMWF-HRES.For the gust speed,the performance of ENSL is consistent with ANN(best individual model) in the whole dataset,whereas ENSL outperforms on extreme gust samples(42.7% compared with 38.7% obtained by ECMWF-HRES in terms of RMSE reduction).Sensitivity analysis of predictors in the four categories shows that ENSL fits their feature importance rankings and physical explanations effectively.

From computer vision to short text understanding: Applying similar approaches into different disciplines

With the development of IoT and 5G technologies,more and more online resources are presented in trendy multimodal data forms over the Internet.Hence,effectively processing multimodal information is significant to the development of various online applications,including e-learning and digital health,to just name a few.However,most AI-driven systems or models can only handle limited forms of information.In this study,we investigate the correlation between natural language processing(NLP)and pattern recognition,trying to apply the mainstream approaches and models used in the computer vision(CV)to the task of NLP.Based on two different Twitter datasets,we propose a convolutional neural network based model to interpret the content of short text with different goals and application backgrounds.The experiments have demonstrated that our proposed model shows fairly competitive performance compared to the mainstream recurrent neural network based NLP models such as bidirectional long short-term memory(Bi-LSTM)and bidirectional gate recurrent unit(Bi-GRU).Moreover,the experimental results also demonstrate that the proposed model can precisely locate the key information in the given text.

Neighbourhood systems based attribute reduction in formal decision contexts

Attribute reduction of formal decision context mainly uses the relationship between two concept lattices generated by the condition and decision attributes to remove redundant condition attributes.By using decision attributes to observe the covering of objects,this study defines two types of consistent sets and reducts in a consistent formal decision context based on neighbourhood systems.Four types of reductions in inconsistent formal decision contexts are also studied.The methods to calculate all types of reductions are formulated by discernibility matrix.Finally,an approach to obtain the decision rules in consistent formal decision context is proposed.

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

Purpose-Steady-state visual evoked potential(SSVEP)has been widely used in the application of electroencephalogram(EEG)based non-invasive brain computer interface(BCI)due to its characteristics of high accuracy and information transfer rate(ITR).To recognize the SSVEP components in collected EEG trials,a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years.In this paper,a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.Design/methodology/approach-To survey and compare the recently proposed recognition algorithms for SSVEP,this paper regarded the conventional canonical correlated analysis(CCA)as the baseline,and selected individual template CCA(ITCCA),multi-set CCA(MsetCCA),task related component analysis(TRCA),latent common source extraction(LCSE)and a sum of squared correlation(SSCOR)for comparison.Findings-For the horizontal comparative of the six surveyed recognition algorithms,this paper adopted the“Tsinghua JFPM-SSVEP”data set and compared the average recognition performance on such data set.The comparative contents including:recognition accuracy,ITR,correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation.Based on the optimal time duration of stimulus presentation,the author has also compared the efficiency of the six compared algorithms.To measure the influence of different parameters,the number of training trials,the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.Originality/value-Based on the comparative results,this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes,realtime and computational complexity.Finally,the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.

Ramp Scheme Based on CRT for Polynomial Ring over Finite Field

Chinese Reminder Theorem(CRT)for integers has been widely used to construct secret sharing schemes for different scenarios,but these schemes have lower information rates than that of Lagrange interpolation-based schemes.In ASIACRYPT 2018,Ning,et al.constructed a perfect(r,n)-threshold scheme based on CRT for polynomial ring over finite field,and the corresponding information rate is one which is the greatest case for a(r,n)-threshold scheme.However,for many practical purposes,the information rate of Ning,et al.scheme is low and perfect security is too much security.In this work,the authors generalize the Ning,et al.(r,n)-threshold scheme to a(t,r,n)-ramp scheme based on CRT for polynomial ring over finite field,which attains the greatest information rate(r−t)for a(t,r,n)-ramp scheme.Moreover,for any given 2≤r_(1)<r_(2)≤n,the ramp scheme can be used to construct a(r_(1),n)-threshold scheme that is threshold changeable to(r′,n)-threshold scheme for all r′∈{r_(1)+1,r_(1)+2,···,r_(2)}.The threshold changeable secret sharing(TCSS)scheme has a greater information rate than other existing TCSS schemes of this type.

Acollaborative target tracking algorithm formultiple UAVs with inferior tracking capabilities

Target tracking is one of the hottest topics in the field of drone research.In this paper,we study the multiple unmanned aerial vehicles(multi-UAV)collaborative target tracking problem.We propose a novel tracking method based on intention estimation and effective cooperation for UAVs with inferior tracking capabilities to track the targets that may have agile,uncertain,and intelligent motion.For three classic target motion modes,we first design a novel trajectory feature extraction method with the least dimension and maximum coverage constraints,and propose an intention estimation mechanism based on the environment and target trajectory features.We propose a novel Voronoi diagram,called MDA-Voronoi,which divides the area with obstacles according to the minimum reachable distance and the minimum steering angle of each UAV.In each MDA-Voronoi region,the maximum reachable region of each UAV is defined,the upper and lower bounds of the trajectory coverage probability are analyzed,and the tracking strategies of the UAVs are designed to effectively reduce the tracking gaps to improve the target sensing time.Then,we use the Nash Q-learning method to design the UAVs’collaborative tracking strategy,considering factors such as collision avoidance,maneuvering constraints,tracking cost,sensing performance,and path overlap.By designing the reward mechanism,the optimal action strategies are obtained as the control input of the UAVs.Finally,simulation analyses are provided to validate our method,and the results demonstrate that the algorithm can improve the collaborative target tracking performance for multiple UAVs with inferior tracking capabilities.

Verifiable image revision from chameleon hashes

In a digital society,the rapid development of computer science and the Internet has greatly facilitated image applications.However,one of the public network also brings risks to both image tampering and privacy exposure.Image authentication is the most important approaches to verify image integrity and authenticity.However,it has been challenging for image authentication to address both issues of tampering detection and privacy protection.One aspect,image authentication requires image contents not be changed to detect tampering.The other,privacy protection needs to remove sensitive information from images,and as a result,the contents should be changed.In this paper,we propose a practical image authentication scheme constructed from chameleon hashes combined with ordinary digital signatures to make tradeoff between tampering detection and privacy protection.Our scheme allows legitimate users to modify contents of authenticated images with a privacy-aware purpose(for example,cover some sensitive areas with mosaics)according to specific rules and verify the authenticity without interaction with the original authenticator.The security of our scheme is guaranteed by the security of the underlying cryptographic primitives.Experiment results show that our scheme is efficient and practical.We believe that our work will facilitate image applications where both authentication and privacy protection are desirable.

Verifiable image revision from chameleon hashes

In a digital society,the rapid development of computer science and the Internet has greatly facilitated image applications.However,one of the public network also brings risks to both image tampering and privacy exposure.Image authentication is the most important approaches to verify image integrity and authenticity.However,it has been cha卜lenging for image authentication to address both issues of tampering detection and privacy protection.One aspect,image authentication requires image contents not be changed to detect tampering.The other,privacy protection needs to remove sensitive information from images,and as a result,the contents should be changed.In this paper,we propose a practical image authentication scheme constructed from chameleon hashes combined with ordinary digital signatures to make tradeoff between tampering detection and privacy protection.Our scheme allows legitimate users to modify contents of authenticated images with a privacy-aware purpose(for example,cover some sensitive areas with mosaics)according to specific rules and verify the authenticity without interaction with the original authenticator.The security of our scheme is guaranteed by the security of the underlying cryptographic primitives.Experiment results show that our scheme is efficient and practical.We believe that our work will facilitate image applications where both authentication and privacy protection are desirable.

Research on clothing patterns generation based on multi-scales self-attention improved generative adversarial network

Purpose-Clothing patterns play a dominant role in costume design and have become an important link in the perception of costume art.Conventional clothing patterns design relies on experienced designers.Although the quality of clothing patterns is very high on conventional design,the input time and output amount ratio is relative low for conventional design.In order to break through the bottleneck of conventional clothing patterns design,this paper proposes a novel way based on generative adversarial network(GAN)model for automatic clothing patterns generation,which not only reduces the dependence of experienced designer,but also improve the input-output ratio.Design/methodology/approach-In view of the fact that clothing patterns have high requirements for global artistic perception and local texture details,this paper improves the conventional GAN model from two aspects:a multi-scales discriminators strategy is introduced to deal with the local texture details;and the selfattention mechanism is introduced to improve the global artistic perception.Therefore,the improved GAN called multi-scales self-attention improved generative adversarial network(MS-SA-GAN)model,which is used for high resolution clothing patterns generation.Findings-To verify the feasibility and effectiveness of the proposed MS-SA-GAN model,a crawler is designed to acquire standard clothing patterns dataset from Baidu pictures,and a comparative experiment is conducted on our designed clothing patterns dataset.In experiments,we have adjusted different parameters of the proposed MS-SA-GAN model,and compared the global artistic perception and local texture details of the generated clothing patterns.Originality/value-Experimental results have shown that the clothing patterns generated by the proposed MS-SA-GANmodel are superior to the conventional algorithms in some local texture detail indexes.In addition,a group of clothing design professionals is invited to evaluate the global artistic perception through a valencearousal scale.The scale results have shown that the proposed MS-SA-GAN model achieves a better global art perception.