With the widespread adoption of encrypted Domain Name System(DNS)technologies such as DNS over Hyper Text Transfer Protocol Secure(HTTPS),traditional port and protocol-based traffic analysis methods have become ineffe...With the widespread adoption of encrypted Domain Name System(DNS)technologies such as DNS over Hyper Text Transfer Protocol Secure(HTTPS),traditional port and protocol-based traffic analysis methods have become ineffective.Although encrypted DNS enhances user privacy protection,it also provides concealed communication channels for malicious software,compelling detection technologies to shift towards statistical featurebased and machine learning approaches.However,these methods still face challenges in real-time performance and privacy protection.This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection.Firstly,a hierarchical architecture of cloud-edge-end collaboration is designed,incorporating task offloading strategies to balance privacy protection and identification efficiency.Secondly,a privacy-preserving federated learning mechanismbased on Federated Robust Aggregation(FedRA)is proposed,utilizingMedoid aggregation and differential privacy techniques to ensure data privacy and enhance identification accuracy.Finally,an edge offloading strategy based on a dynamic priority scheduling algorithm(DPSA)is designed to alleviate terminal burden and reduce latency.Simulation results demonstrate that the proposed technology significantly improves the accuracy and realtime performance of encrypted DNS traffic identification while protecting privacy,making it suitable for various network environments.展开更多
A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,whic...A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,which is used for the scrambling,substitution and diffusion processes.The three-dimensional Fisher-Yates scrambling,S-box substitution and diffusion are employed for the first round of encryption.The chaotic sequence is adopted for secondary encryption to scramble the ciphertext obtained in the first round.Then,three-dimensional filter is applied to diffusion for further useful information hiding.The key to the algorithm is generated by the combination of hash value of plaintext image and the input parameters.It improves resisting ability of plaintext attacks.The security analysis shows that the algorithm is effective and efficient.It can resist common attacks.In addition,the good diffusion effect shows that the scheme can solve the differential attacks encountered in the transmission of medical images and has positive implications for future research.展开更多
This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals noth...This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals nothing about the predicate f,as long as f is drawn from an evasive distribution with sufficient entropy.The proposed scheme extends the group-based public-key function-private predicate encryption(FP-PE)for“small superset predicates”proposed by Bartusek et al.(Asiacrypt 19),to the setting of inner-product predicates.This is the first construction of public-key FP-PE with enhanced function privacy security beyond the equality predicates,which is previously proposed by Boneh et al.(CRYPTO 13).The proposed construction relies on bilinear groups,and the security is proved in the generic bilinear group model.展开更多
The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for he...The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.展开更多
Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does no...Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.展开更多
This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevale...This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevalent,secure and efficient data transmission becomes crucial.The proposed algorithm addresses this need by offering a robust yet resource-efficient solution for image encryption.Traditional image encryption relies on confusion and diffusion steps.These stages are generally implemented linearly,but this work introduces a new RSP(Random Strip Peeling)algorithm for the confusion step,which disrupts linearity in the lightweight category by using two different sequences generated by the 1D Tent Map with varying initial conditions.The diffusion stage then employs an XOR matrix generated by the Logistic Map.Different evaluation metrics,such as entropy analysis,key sensitivity,statistical and differential attacks resistance,and robustness analysis demonstrate the proposed algorithm's lightweight,robust,and efficient.The proposed encryption scheme achieved average metric values of 99.6056 for NPCR,33.4397 for UACI,and 7.9914 for information entropy in the SIPI image dataset.It also exhibits a time complexity of O(2×M×N)for an image of size M×N.展开更多
As data analysis often incurs significant communication and computational costs,these tasks are increasingly outsourced to cloud computing platforms.However,this introduces privacy concerns,as sensitive data must be t...As data analysis often incurs significant communication and computational costs,these tasks are increasingly outsourced to cloud computing platforms.However,this introduces privacy concerns,as sensitive data must be transmitted to and processed by untrusted parties.To address this,fully homomorphic encryption(FHE)has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service(MLaaS),enabling computation on encrypted data without revealing the plaintext.Nevertheless,FHE remains computationally expensive.As a result,approximate homomorphic encryption(AHE)schemes,such as CKKS,have attracted attention due to their efficiency.In our previous work,we proposed RP-OKC,a CKKS-based clustering scheme implemented via TenSEAL.However,errors inherent to CKKS operations—termed CKKS-errors—can affect the accuracy of the result after decryption.Since these errors can be mitigated through post-decryption rounding,we propose a data pre-scaling technique to increase the number of significant digits and reduce CKKS-errors.Furthermore,we introduce an Operation-Error-Estimation(OEE)table that quantifies upper-bound error estimates for various CKKS operations.This table enables error-aware decryption correction,ensuring alignment between encrypted and plaintext results.We validate our method on K-means clustering using the Kaggle Customer Segmentation dataset.Experimental results confirm that the proposed scheme enhances the accuracy and reliability of privacy-preserving data analysis in cloud environments.展开更多
With the rapid development of information technology,data security issues have received increasing attention.Data encryption and decryption technology,as a key means of ensuring data security,plays an important role i...With the rapid development of information technology,data security issues have received increasing attention.Data encryption and decryption technology,as a key means of ensuring data security,plays an important role in multiple fields such as communication security,data storage,and data recovery.This article explores the fundamental principles and interrelationships of data encryption and decryption,examines the strengths,weaknesses,and applicability of symmetric,asymmetric,and hybrid encryption algorithms,and introduces key application scenarios for data encryption and decryption technology.It examines the challenges and corresponding countermeasures related to encryption algorithm security,key management,and encryption-decryption performance.Finally,it analyzes the development trends and future prospects of data encryption and decryption technology.This article provides a systematic understanding of data encryption and decryption techniques,which has good reference value for software designers.展开更多
Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive da...Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and misuse.With the exponential growth of digital data,robust security measures are essential.Data encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key control.Despite their individual benefits,both require significant computational resources.Additionally,performing them separately for the same data increases complexity and processing time.Recognizing the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and efficiency.Thealgorithmoperates on 128-bit block sizes and a 256-bit secret key length.It combines Huffman coding for compression and a Tent map for encryption.Additionally,an iterative Arnold cat map further enhances cryptographic confusion properties.Experimental analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.展开更多
Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematica...Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematical and computer modeling of a novel two-dimensional(2D)chaotic system for secure key generation in quantum image encryption(QIE).The proposed map employs trigonometric perturbations in conjunction with rational-saturation functions and hence,named as Trigonometric-Rational-Saturation(TRS)map.Through rigorous mathematical analysis and computational simulations,the map is extensively evaluated for bifurcation behaviour,chaotic trajectories,and Lyapunov exponents.The security evaluation validates the map’s non-linearity,unpredictability,and sensitive dependence on initial conditions.In addition,the proposed TRS map has further been tested by integrating it in a QIE scheme.The QIE scheme first quantum-encodes the classic image using the Novel Enhanced Quantum Representation(NEQR)technique,the TRS map is used for the generation of secure diffusion key,which is XOR-ed with the quantum-ready image to obtain the encrypted images.The security evaluation of the QIE scheme demonstrates superior security of the encrypted images in terms of statistical security attacks and also against Differential attacks.The encrypted images exhibit zero correlation and maximum entropy with demonstrating strong resilience due to 99.62%and 33.47%results for Number of Pixels Change Rate(NPCR)and Unified Average Changing Intensity(UACI).The results validate the effectiveness of TRS-based quantum encryption scheme in securing digital images against emerging quantum threats,making it suitable for secure image encryption in IoT and edge-based applications.展开更多
A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built...A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.展开更多
With the rapid development of holographic technology,metasurface-based holographic communication schemes have demonstrated immense potential for electromagnetic(EM)multifunctionality.However,traditional passive metasu...With the rapid development of holographic technology,metasurface-based holographic communication schemes have demonstrated immense potential for electromagnetic(EM)multifunctionality.However,traditional passive metasurfaces are severely limited by their lack of reconfigurability,hindering the realization of versatile holographic applications.Origami,an art form that mechanically induces spatial deformations,serves as a platform for multifunctional devices and has garnered significant attention in optics,physics,and materials science.The Miura-ori folding paradigm,characterized by its continuous reconfigurability in folded states,remains unexplored in the context of holographic imaging.Herein,we integrate the principles of Rosenfeld with L-and D-metal chiral enantiomers on a Miura-ori surface to tailor the aperture distribution.Leveraging the continuously tunable nature of the Miura-ori's folded states,the chiral response of the metallic structures varies across different folding configurations,enabling distinct EM holographic imaging functionalities.In the planar state,holographic encryption is achieved.Under specific folding conditions and driven by spin circularly polarized(CP)waves at a particular frequency,multiplexed holographic images can be reconstructed on designated focal planes with CP selectivity.Notably,the fabricated origami metasurface exhibits a large negative Poisson ratio,facilitating portability and deployment and offering novel avenues for spin-selective systems,camouflage,and information encryption.展开更多
Due to the rapid advancement of information technology,data has emerged as the core resource driving decision-making and innovation across all industries.As the foundation of artificial intelligence,machine learning(M...Due to the rapid advancement of information technology,data has emerged as the core resource driving decision-making and innovation across all industries.As the foundation of artificial intelligence,machine learning(ML)has expanded its applications into intelligent recommendation systems,autonomous driving,medical diagnosis,and financial risk assessment.However,it relies on massive datasets,which contain sensitive personal information.Consequently,Privacy-Preserving Machine Learning(PPML)has become a critical research direction.To address the challenges of efficiency and accuracy in encrypted data computation within PPML,Homomorphic Encryption(HE)technology is a crucial solution,owing to its capability to facilitate computations on encrypted data.However,the integration of machine learning and homomorphic encryption technologies faces multiple challenges.Against this backdrop,this paper reviews homomorphic encryption technologies,with a focus on the advantages of the Cheon-Kim-Kim-Song(CKKS)algorithm in supporting approximate floating-point computations.This paper reviews the development of three machine learning techniques:K-nearest neighbors(KNN),K-means clustering,and face recognition-in integration with homomorphic encryption.It proposes feasible schemes for typical scenarios,summarizes limitations and future optimization directions.Additionally,it presents a systematic exploration of the integration of homomorphic encryption and machine learning from the essence of the technology,application implementation,performance trade-offs,technological convergence and future pathways to advance technological development.展开更多
Ln-containing polyoxoniobates(PONbs)have appealing applications in luminescence,information encryption and magnetic fields,but the synthesis of PONbs containing high-nuclearity Ln-O clusters is challenging due to the ...Ln-containing polyoxoniobates(PONbs)have appealing applications in luminescence,information encryption and magnetic fields,but the synthesis of PONbs containing high-nuclearity Ln-O clusters is challenging due to the easy hydrolysis of Ln^(3+)ions in alkaline environments.In this paper,we are able to integrate CO_(3)^(2-)and high-nuclearity Ln-O clusters into PONb to construct an inorganic giant Eu_(19)-embedded PONb H_(49)K_(16)Na_(13)(H_(2)O)_(63)[Eu_(21)O_(2)(OH)_(7)(H_(2)O)_(5)(Nb_(7)O_(22))_(10)(Nb_(2)O_(6))_(2)(CO_(3))_(18)]·91H_(2)O(1),which contains the highest nuclearity Eu-O clusters and the largest number of Eu^(3+)ions among PONbs.In addition,the film that was prepared by mixing 1 with gelatin and glycerol,exhibits reversible luminescence switching behavior under acid/alkali stimulation and has been used to create a fluorescence-encoded information approach.This work paves a feasible strategy for the construction of high-nuclearity Ln-O cluster-containing PONbs and the expansion of the application of Ln-containing PONbs in information encryption.展开更多
Ensuring the integrity and confidentiality of patient medical information is a critical priority in the healthcare sector.In the context of security,this paper proposes a novel encryption algorithm that integrates Blo...Ensuring the integrity and confidentiality of patient medical information is a critical priority in the healthcare sector.In the context of security,this paper proposes a novel encryption algorithm that integrates Blockchain technology,aiming to improve the security and privacy of transmitted data.The proposed encryption algorithm is a block-cipher image encryption scheme based on different chaotic maps:The logistic Map,the Tent Map,and the Henon Map used to generate three encryption keys.The proposed block-cipher system employs the Hilbert curve to perform permutation while a generated chaos-based S-Box is used to perform substitution.Furthermore,the integration of a Blockchain-based solution for securing data transmission and communication between nodes and authenticating the encrypted medical image’s authenticity adds a layer of security to our proposed method.Our proposed cryptosystem is divided into two principal modules presented as a pseudo-random number generator(PRNG)used for key generation and an encryption and decryption system based on the properties of confusion and diffusion.The security analysis and experimental tests for the proposed algorithm show that the average value of the information entropy of the encrypted images is 7.9993,the Number of Pixels Change Rate(NPCR)values are over 99.5%and the Unified Average Changing Intensity(UACI)values are greater than 33%.These results prove the strength of our proposed approach,demonstrating that it can significantly enhance the security of encrypted images.展开更多
Ciphertext data retrieval in cloud databases suffers from some critical limitations,such as inadequate security measures,disorganized key management practices,and insufficient retrieval access control capabilities.To ...Ciphertext data retrieval in cloud databases suffers from some critical limitations,such as inadequate security measures,disorganized key management practices,and insufficient retrieval access control capabilities.To address these problems,this paper proposes an enhanced Fully Homomorphic Encryption(FHE)algorithm based on an improved DGHV algorithm,coupled with an optimized ciphertext retrieval scheme.Our specific contributions are outlined as follows:First,we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data.Second,a triple-key encryption mechanism,which separates the data encryption key,retrieval authorization key,and retrieval key,is designed.Different keys are provided to different entities to run corresponding system functions.The key separation architecture proves particularly advantageous in multi-verifier coexistence scenarios,environments involving untrusted third-party retrieval services.Finally,the enhanced DGHV-based retrieval mechanism extends conventional functionality by enabling multi-keyword queries with similarity-ranked results,thereby significantly improving both the functionality and usability of the FHE system.展开更多
False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unr...False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unreliability,data integrity loss and operational vulnerability exposure.Given its widespread harm and impact,conducting in-depth research on FDIA detection is vitally important.This paper innovatively introduces a FDIA detection scheme:A Protected Federated Deep Learning(ProFed),which leverages Federated Averaging algorithm(FedAvg)as a foundational framework to fortify data security,harnesses pre-trained enhanced spatial-temporal graph neural networks(STGNN)to perform localized model training and integrates the Cheon-Kim-Kim-Song(CKKS)homomorphic encryption system to secure sensitive information.Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics,with peak improvements reaching up to 35%.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation...Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.展开更多
Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes ...Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.展开更多
文摘With the widespread adoption of encrypted Domain Name System(DNS)technologies such as DNS over Hyper Text Transfer Protocol Secure(HTTPS),traditional port and protocol-based traffic analysis methods have become ineffective.Although encrypted DNS enhances user privacy protection,it also provides concealed communication channels for malicious software,compelling detection technologies to shift towards statistical featurebased and machine learning approaches.However,these methods still face challenges in real-time performance and privacy protection.This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection.Firstly,a hierarchical architecture of cloud-edge-end collaboration is designed,incorporating task offloading strategies to balance privacy protection and identification efficiency.Secondly,a privacy-preserving federated learning mechanismbased on Federated Robust Aggregation(FedRA)is proposed,utilizingMedoid aggregation and differential privacy techniques to ensure data privacy and enhance identification accuracy.Finally,an edge offloading strategy based on a dynamic priority scheduling algorithm(DPSA)is designed to alleviate terminal burden and reduce latency.Simulation results demonstrate that the proposed technology significantly improves the accuracy and realtime performance of encrypted DNS traffic identification while protecting privacy,making it suitable for various network environments.
文摘A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,which is used for the scrambling,substitution and diffusion processes.The three-dimensional Fisher-Yates scrambling,S-box substitution and diffusion are employed for the first round of encryption.The chaotic sequence is adopted for secondary encryption to scramble the ciphertext obtained in the first round.Then,three-dimensional filter is applied to diffusion for further useful information hiding.The key to the algorithm is generated by the combination of hash value of plaintext image and the input parameters.It improves resisting ability of plaintext attacks.The security analysis shows that the algorithm is effective and efficient.It can resist common attacks.In addition,the good diffusion effect shows that the scheme can solve the differential attacks encountered in the transmission of medical images and has positive implications for future research.
基金National Key Research and Development Program of China(2021YFB3101402)National Natural Science Foundation of China(62202294)。
文摘This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals nothing about the predicate f,as long as f is drawn from an evasive distribution with sufficient entropy.The proposed scheme extends the group-based public-key function-private predicate encryption(FP-PE)for“small superset predicates”proposed by Bartusek et al.(Asiacrypt 19),to the setting of inner-product predicates.This is the first construction of public-key FP-PE with enhanced function privacy security beyond the equality predicates,which is previously proposed by Boneh et al.(CRYPTO 13).The proposed construction relies on bilinear groups,and the security is proved in the generic bilinear group model.
基金funded by the ICT Division of theMinistry of Posts,Telecommunications,and Information Technology of Bangladesh under Grant Number 56.00.0000.052.33.005.21-7(Tracking No.22FS15306)support from the University of Rajshahi.
文摘The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.
文摘Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.
基金Türkiye Bilimsel ve Teknolojik Arastırma Kurumu。
文摘This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevalent,secure and efficient data transmission becomes crucial.The proposed algorithm addresses this need by offering a robust yet resource-efficient solution for image encryption.Traditional image encryption relies on confusion and diffusion steps.These stages are generally implemented linearly,but this work introduces a new RSP(Random Strip Peeling)algorithm for the confusion step,which disrupts linearity in the lightweight category by using two different sequences generated by the 1D Tent Map with varying initial conditions.The diffusion stage then employs an XOR matrix generated by the Logistic Map.Different evaluation metrics,such as entropy analysis,key sensitivity,statistical and differential attacks resistance,and robustness analysis demonstrate the proposed algorithm's lightweight,robust,and efficient.The proposed encryption scheme achieved average metric values of 99.6056 for NPCR,33.4397 for UACI,and 7.9914 for information entropy in the SIPI image dataset.It also exhibits a time complexity of O(2×M×N)for an image of size M×N.
基金funded by National Science and Technology Council,Taiwan,grant numbers are 110-2401-H-002-094-MY2 and 112-2221-E-130-001.
文摘As data analysis often incurs significant communication and computational costs,these tasks are increasingly outsourced to cloud computing platforms.However,this introduces privacy concerns,as sensitive data must be transmitted to and processed by untrusted parties.To address this,fully homomorphic encryption(FHE)has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service(MLaaS),enabling computation on encrypted data without revealing the plaintext.Nevertheless,FHE remains computationally expensive.As a result,approximate homomorphic encryption(AHE)schemes,such as CKKS,have attracted attention due to their efficiency.In our previous work,we proposed RP-OKC,a CKKS-based clustering scheme implemented via TenSEAL.However,errors inherent to CKKS operations—termed CKKS-errors—can affect the accuracy of the result after decryption.Since these errors can be mitigated through post-decryption rounding,we propose a data pre-scaling technique to increase the number of significant digits and reduce CKKS-errors.Furthermore,we introduce an Operation-Error-Estimation(OEE)table that quantifies upper-bound error estimates for various CKKS operations.This table enables error-aware decryption correction,ensuring alignment between encrypted and plaintext results.We validate our method on K-means clustering using the Kaggle Customer Segmentation dataset.Experimental results confirm that the proposed scheme enhances the accuracy and reliability of privacy-preserving data analysis in cloud environments.
文摘With the rapid development of information technology,data security issues have received increasing attention.Data encryption and decryption technology,as a key means of ensuring data security,plays an important role in multiple fields such as communication security,data storage,and data recovery.This article explores the fundamental principles and interrelationships of data encryption and decryption,examines the strengths,weaknesses,and applicability of symmetric,asymmetric,and hybrid encryption algorithms,and introduces key application scenarios for data encryption and decryption technology.It examines the challenges and corresponding countermeasures related to encryption algorithm security,key management,and encryption-decryption performance.Finally,it analyzes the development trends and future prospects of data encryption and decryption technology.This article provides a systematic understanding of data encryption and decryption techniques,which has good reference value for software designers.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and misuse.With the exponential growth of digital data,robust security measures are essential.Data encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key control.Despite their individual benefits,both require significant computational resources.Additionally,performing them separately for the same data increases complexity and processing time.Recognizing the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and efficiency.Thealgorithmoperates on 128-bit block sizes and a 256-bit secret key length.It combines Huffman coding for compression and a Tent map for encryption.Additionally,an iterative Arnold cat map further enhances cryptographic confusion properties.Experimental analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.
基金funded by Deanship of Research and Graduate Studies at King Khalid University.The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP.2/556/45).
文摘Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematical and computer modeling of a novel two-dimensional(2D)chaotic system for secure key generation in quantum image encryption(QIE).The proposed map employs trigonometric perturbations in conjunction with rational-saturation functions and hence,named as Trigonometric-Rational-Saturation(TRS)map.Through rigorous mathematical analysis and computational simulations,the map is extensively evaluated for bifurcation behaviour,chaotic trajectories,and Lyapunov exponents.The security evaluation validates the map’s non-linearity,unpredictability,and sensitive dependence on initial conditions.In addition,the proposed TRS map has further been tested by integrating it in a QIE scheme.The QIE scheme first quantum-encodes the classic image using the Novel Enhanced Quantum Representation(NEQR)technique,the TRS map is used for the generation of secure diffusion key,which is XOR-ed with the quantum-ready image to obtain the encrypted images.The security evaluation of the QIE scheme demonstrates superior security of the encrypted images in terms of statistical security attacks and also against Differential attacks.The encrypted images exhibit zero correlation and maximum entropy with demonstrating strong resilience due to 99.62%and 33.47%results for Number of Pixels Change Rate(NPCR)and Unified Average Changing Intensity(UACI).The results validate the effectiveness of TRS-based quantum encryption scheme in securing digital images against emerging quantum threats,making it suitable for secure image encryption in IoT and edge-based applications.
基金funded by Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number (PNURSP2024R408), Princess Nourah bint AbdulrahmanUniversity, Riyadh, Saudi Arabia.
文摘A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.
基金financial supports from National Key Research and Development Program of China(No.2022YFB3806200)。
文摘With the rapid development of holographic technology,metasurface-based holographic communication schemes have demonstrated immense potential for electromagnetic(EM)multifunctionality.However,traditional passive metasurfaces are severely limited by their lack of reconfigurability,hindering the realization of versatile holographic applications.Origami,an art form that mechanically induces spatial deformations,serves as a platform for multifunctional devices and has garnered significant attention in optics,physics,and materials science.The Miura-ori folding paradigm,characterized by its continuous reconfigurability in folded states,remains unexplored in the context of holographic imaging.Herein,we integrate the principles of Rosenfeld with L-and D-metal chiral enantiomers on a Miura-ori surface to tailor the aperture distribution.Leveraging the continuously tunable nature of the Miura-ori's folded states,the chiral response of the metallic structures varies across different folding configurations,enabling distinct EM holographic imaging functionalities.In the planar state,holographic encryption is achieved.Under specific folding conditions and driven by spin circularly polarized(CP)waves at a particular frequency,multiplexed holographic images can be reconstructed on designated focal planes with CP selectivity.Notably,the fabricated origami metasurface exhibits a large negative Poisson ratio,facilitating portability and deployment and offering novel avenues for spin-selective systems,camouflage,and information encryption.
基金supported by the fllowing projects:Natural Science Foundation of China under Grant 62172436Self-Initiated Scientific Research Project of the Chinese People's Armed Police Force under Grant ZZKY20243129Basic Frontier Innovation Project of the Engineering University of the Chinese People's Armed Police Force under Grant WJY202421.
文摘Due to the rapid advancement of information technology,data has emerged as the core resource driving decision-making and innovation across all industries.As the foundation of artificial intelligence,machine learning(ML)has expanded its applications into intelligent recommendation systems,autonomous driving,medical diagnosis,and financial risk assessment.However,it relies on massive datasets,which contain sensitive personal information.Consequently,Privacy-Preserving Machine Learning(PPML)has become a critical research direction.To address the challenges of efficiency and accuracy in encrypted data computation within PPML,Homomorphic Encryption(HE)technology is a crucial solution,owing to its capability to facilitate computations on encrypted data.However,the integration of machine learning and homomorphic encryption technologies faces multiple challenges.Against this backdrop,this paper reviews homomorphic encryption technologies,with a focus on the advantages of the Cheon-Kim-Kim-Song(CKKS)algorithm in supporting approximate floating-point computations.This paper reviews the development of three machine learning techniques:K-nearest neighbors(KNN),K-means clustering,and face recognition-in integration with homomorphic encryption.It proposes feasible schemes for typical scenarios,summarizes limitations and future optimization directions.Additionally,it presents a systematic exploration of the integration of homomorphic encryption and machine learning from the essence of the technology,application implementation,performance trade-offs,technological convergence and future pathways to advance technological development.
基金the financial support from the National Natural Science Foundation of China(Nos.21971040,22171045,and 22371046)。
文摘Ln-containing polyoxoniobates(PONbs)have appealing applications in luminescence,information encryption and magnetic fields,but the synthesis of PONbs containing high-nuclearity Ln-O clusters is challenging due to the easy hydrolysis of Ln^(3+)ions in alkaline environments.In this paper,we are able to integrate CO_(3)^(2-)and high-nuclearity Ln-O clusters into PONb to construct an inorganic giant Eu_(19)-embedded PONb H_(49)K_(16)Na_(13)(H_(2)O)_(63)[Eu_(21)O_(2)(OH)_(7)(H_(2)O)_(5)(Nb_(7)O_(22))_(10)(Nb_(2)O_(6))_(2)(CO_(3))_(18)]·91H_(2)O(1),which contains the highest nuclearity Eu-O clusters and the largest number of Eu^(3+)ions among PONbs.In addition,the film that was prepared by mixing 1 with gelatin and glycerol,exhibits reversible luminescence switching behavior under acid/alkali stimulation and has been used to create a fluorescence-encoded information approach.This work paves a feasible strategy for the construction of high-nuclearity Ln-O cluster-containing PONbs and the expansion of the application of Ln-containing PONbs in information encryption.
基金supported by the Large Group Project under grant number(RGP2/473/46).
文摘Ensuring the integrity and confidentiality of patient medical information is a critical priority in the healthcare sector.In the context of security,this paper proposes a novel encryption algorithm that integrates Blockchain technology,aiming to improve the security and privacy of transmitted data.The proposed encryption algorithm is a block-cipher image encryption scheme based on different chaotic maps:The logistic Map,the Tent Map,and the Henon Map used to generate three encryption keys.The proposed block-cipher system employs the Hilbert curve to perform permutation while a generated chaos-based S-Box is used to perform substitution.Furthermore,the integration of a Blockchain-based solution for securing data transmission and communication between nodes and authenticating the encrypted medical image’s authenticity adds a layer of security to our proposed method.Our proposed cryptosystem is divided into two principal modules presented as a pseudo-random number generator(PRNG)used for key generation and an encryption and decryption system based on the properties of confusion and diffusion.The security analysis and experimental tests for the proposed algorithm show that the average value of the information entropy of the encrypted images is 7.9993,the Number of Pixels Change Rate(NPCR)values are over 99.5%and the Unified Average Changing Intensity(UACI)values are greater than 33%.These results prove the strength of our proposed approach,demonstrating that it can significantly enhance the security of encrypted images.
基金supported by the Innovation Program for Quantum Science and technology(2021ZD0301300)supported by the Fundamental Research Funds for the Central Universities(Nos.3282024046,3282024052,3282024058,3282023017).
文摘Ciphertext data retrieval in cloud databases suffers from some critical limitations,such as inadequate security measures,disorganized key management practices,and insufficient retrieval access control capabilities.To address these problems,this paper proposes an enhanced Fully Homomorphic Encryption(FHE)algorithm based on an improved DGHV algorithm,coupled with an optimized ciphertext retrieval scheme.Our specific contributions are outlined as follows:First,we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data.Second,a triple-key encryption mechanism,which separates the data encryption key,retrieval authorization key,and retrieval key,is designed.Different keys are provided to different entities to run corresponding system functions.The key separation architecture proves particularly advantageous in multi-verifier coexistence scenarios,environments involving untrusted third-party retrieval services.Finally,the enhanced DGHV-based retrieval mechanism extends conventional functionality by enabling multi-keyword queries with similarity-ranked results,thereby significantly improving both the functionality and usability of the FHE system.
基金supported in part by the Sichuan Science and Technology Program(2024YFHZ0015)the Key Laboratory of Data Protection and Intelligent Management,Ministry of Education,Sichuan University(SCUSACXYD202401).
文摘False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unreliability,data integrity loss and operational vulnerability exposure.Given its widespread harm and impact,conducting in-depth research on FDIA detection is vitally important.This paper innovatively introduces a FDIA detection scheme:A Protected Federated Deep Learning(ProFed),which leverages Federated Averaging algorithm(FedAvg)as a foundational framework to fortify data security,harnesses pre-trained enhanced spatial-temporal graph neural networks(STGNN)to perform localized model training and integrates the Cheon-Kim-Kim-Song(CKKS)homomorphic encryption system to secure sensitive information.Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics,with peak improvements reaching up to 35%.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
文摘Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.
基金supported by the National Natural Science Foundation of China(62376106)The Science and Technology Development Plan of Jilin Province(20250102212JC).
文摘Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.
