Blockchain technology offers immutable evidence in decentralized data management in modern medical systems. However, securing Electronic Medical Records (EMR) within the blockchain framework remains a critical challenge due to the inherent storage limitations and computational overhead from existing public key methods. To address such issues, this paper proposes a certificateless hybrid signcryption scheme that integrates a tag-based key encapsulation mechanism (tag-KEM) with a data encapsulation mechanism (DEM), to enable secure and efficient EMR data management. Our approach addresses key limitations in existing blockchain systems by combining lightweight cryptographic operations based on Elliptic Curve Cryptography(ECC) with efficient decentralized off-chain storage to accommodate large-scale on growing health data. By employing ECC operations, the scheme significantly reduces computational complexity and enhances blockchain transaction throughput. Results from our experimental analysis shows a 40.01%–97.71% reduction in computational cost compared to the other existing methods. Additionally, findings from the decentralized off-chain analysis indicate significant improvements in storage efficiency and system performance, underscoring the scheme’s practicality for real-world data security applications.

The growing adoption of electronic personal health records (ePHRs) demands cryptographic solutions that ensure secure and efficient data access. Threshold cryptography provides a framework for controlled multi-party access, yet existing schemes face practical limitations. Many require trusted key dealers, creating single points of failure and key escrow vulnerabilities, while others rely on pairing-based constructions that scale poorly. Furthermore, batch-oriented processing in previous schemes fails to support individual on-demand access patterns typical in healthcare applications. We propose a Distributed Identity-Based Threshold Decryption (DIBTD) scheme that addresses these limitations. First, our protocol removes all trusted setup assumptions through a fully distributed key generation mechanism based on verifiable secret sharing. Second, it achieves constant-time encryption and decryption operations, independent of committee size, by using efficient elliptic curve operations on secp256k1 rather than computationally heavy pairings, yielding up to 56× faster encryption than prior work. Third, DIBTD integrates the detection of malicious actors via zero-knowledge proofs, allowing the dynamic exclusion of compromised participants during system initialization. We provide formal security proofs showing the security of IND-CCA2 in the random oracle model under the discrete logarithm of the elliptic curve (ECDLP) and computational Diffie-Hellman (CDH) assumptions. The scheme remains secure against adaptive adversaries that control up to$t-1$participants. Experimental evaluation demonstrates practical efficiency: ciphertexts of only 86 bytes, constant 33-byte public keys, and sub-millisecond encryption latency. A pure Rust implementation on commodity hardware achieves 0.065ms per patient record while maintaining 128-bit security.

In sharding systems, account migration is viewed as an effective strategy to minimize the proportion of cross-shard transactions and optimize load balancing between shards. However, existing account migration schemes typically require locking the transactions of the target account, which leads to delayed transaction confirmations and reduced system effi-ciency. To address this challenge, our paper proposes a secure and efficient account migration protocol that utilizes a nonce mechanism to eliminate the need for locking operations on the target account, thereby mitigating the impact of migration on confirmation delays for related transactions. Furthermore, we introduce a failure handling mechanism to ensure the consistency and stability of the system. Finally, we assess the feasibility of our protocol and conduct relevant experimental validation.

Fraud detection in blockchain networks is challenging due to the dynamic and complex interactions between entities. Most existing fraud detection methods rely on static graphs and often struggle to account for the dynamic evolution of fraudulent behaviors. This paper introduces a dynamic Enhanced Temporal Graph Network (ETGN) model that leverages graph convolutional networks (GCNs) and recurrent graph units (GRU) to capture structural and temporal dependencies for identifying anomalous edges in blockchain transactions. The proposed model integrates Modified-GCN-based GRUs (MGGRU) for capturing temporal dependencies, multi-head attention for feature enhancement, a dropout layer to avoid overfitting, and a Multi-layer Perceptron (MLP) edge classifier for robust abnormality detection. We further present a novel graph construction using ego-nets and a labeling pipeline that leverages node-centric centrality measures to assign fraud scores, classify edges, and predict anomalous behaviors in the network. The model is validated on real-world blockchain data, including three versions of Ethereum’s ERC20 and Bitcoin transaction network, where it achieves competitive performance metrics such as AUC, precision, recall, and F1 scores. Comprehensive experiments demonstrate the capability of ETGN to detect fraudulent interactions across time slices, providing a robust framework for temporal anomaly detection in transactional blockchain networks.

Named Data Networking (NDN) has proven to be a suitable candidate for Vehicular Ad-hoc Networks (VANETs) because of its data-centric nature and as a worthy replacement for IP addressing, particularly for those with high mobility like VANETs. This has led to the emergence of Vehicular Named Data Networking (VNDN). With the blockchain’s ability to ensure immutability, transparency, accountability, and trust, the combination of blockchain and NDN in the VANETs environment has the propensity to alleviate many security issues in VANETs. This paper introduces a blockchain-enabled NDN framework that guarantees a trustworthy and secure data-sharing network in VNDN. Moreover, we utilized an effective reputation mechanism to facilitate a trustworthy and honest data provision in our mobility network. We also adopted a collaborative caching mechanism to improve our system performance, as caching is one of the pivotal reasons for VNDN. We simulated our work using SUMO and ndnSIM and tested our framework against other related systems. The findings show that our proposed approach enhances performance depending on the parameters used.

Sharding has the potential to overcome the scalability constraints of monolithic blockchains. However, some challenges are associated with sharding, such as optimizing the placement of transactions into shards to minimize cross-shard transactions, balancing workload as shard capacity increases, and identifying shards that process transactions maliciously. To address these challenges, we propose a hierarchical high-performance blockchain (Hiba) architecture. Hiba leverages inter-shard to facilitate cross-shard consensus, where a pre-selected subset of nodes from both transaction originating and receiving shards collaboratively participate in the validation process. This design ensures the validity of transactions and mitigates double-spending risks across various shards. Simultaneously, it reduces validation costs by eliminating the need for all nodes in both shards to actively participate in the consensus process. Additionally, Hiba implements a novel multi-tiered validation system. Following initial validation at the intra-shard and inter-shard levels, a subset of randomly chosen or suspicion-based transactions undergoes further validation through auxiliary consensus. This auxiliary consensus acts as a secondary validation layer, ensuring the integrity of the intra-shard/inter-shard consensus process. To improve transaction processing efficiency, we implement an optimized workload distribution scheme based on fitness functions to minimize the number of cross-shard transactions. The experimental results demonstrate that Hiba surpasses the existing works regarding throughput and latency.

The rapid rise in blockchain technology’s popularity has prompted numerous models to analyze patterns and detect anomalies in blockchain networks based on transaction history. However, most existing studies overlook transactions’ dynamic, nonlinear, and time-variant nature in a time series context. This paper introduces an innovative methodology for enhancing blockchain network performance through advanced time series analysis, anomaly detection, and correction. We propose a hybrid deep learning model integrating Long Short-Term Memory (LSTM) networks, Multi-Head Attention (MHA), and Fully Connected Network (FCN) layers to predict transaction volumes in blockchain networks. The LSTM network captures both short-term and long-term dependencies in blockchain time series data, while the MHA mechanism focuses on relevant input sequence segments. FCN layers perform final feature processing and map the output to predicted transaction volumes. To address overfitting, a Dropout layer is added between the FCN layers. Anomalies are identified using Gaussian Mixture Models (GMM) and corrected via Gaussian Process Regression (GPR). Applied to real-world blockchain transaction datasets, our methodology demonstrates superior efficacy in detecting and correcting anomalies, yielding a more accurate representation of the network’s true behavior. This leads to improved estimates of average and peak throughput and network volatility.

Summary In this work, privacy‐preserving distributed deep learning (PPDDL) is re‐visited with a specific application to diagnosing long‐term illness like diabetic retinopathy. In order to protect the privacy of participants datasets, a multi‐key PPDDL solution is proposed which is robust against collusion attacks and is also post‐quantum robust. Additionally, the PPDDL solution provides robust network security in terms of integrity of transmitted ciphertexts and keys, forward secrecy, and prevention of man‐in‐the‐middle attacks and is extensively verified using Verifpal. Proposed solution is evaluated on retina image datasets to detect diabetic retinopathy, with deep learning accuracy results of 96.30%, 96.21% and 96.20% for DDL, DDL + SINGLE and DDL + MULTI scenarios respectively. Results from our simulation indicate that accuracy of the PPDDL is maintained while protecting the privacy of the datasets of participants. Our proposed solution is also efficient in terms of the communication and run‐time costs.

Named data networking (NDN), which is an implementation of Information-Centric Networking (ICN), has emerged as a promising new direction in networking as a result of the shortcomings of the transmission control protocol/Internet protocol (TCP/IP) architecture. NDN differs from conventional networking protocols since it places more importance on the data than where it originated. Vehicular-named data networking (VNDN) is a game-changing architecture due to its name-based forwarding and in-network caching. VNDN is designed to facilitate effective management of vehicular ad-hoc network (VANET) characteristics such as high mobility, connection intermittency and dynamic topology. However, VNDN’s data verification procedure might lead to lengthy wait times, particularly for mobile and vehicle networks. So, it is sometimes unfit for uses where speed and security are paramount, such as exchanging safety messages. Moreover, the performance of vehicle networks is negatively affected by the longer reaction times brought about by computing-intensive jobs. Therefore, we present a lightweight VNDN-based certificateless signcryption strategy that leverages the hyperelliptic curve cryptosystem’s security hardness and edge computing. Analysis of the scheme’s security and comparisons with similar systems demonstrate its effectiveness. Our suggested approach offers superior security with fewer computing and communication requirements, as verified by the final findings.

Precision agriculture (PA) involves collecting, processing, and analyzing datasets in agriculture for an informed decision. Due to the high data storage and application maintenance costs, farmers usually outsource their agricultural data obtained from PA to cloud service providers to leverage cloud services. Nonetheless, serious security concerns arise from using cloud services for farmers. For instance, an attacker can intercept agricultural data and run comprehensive statistical analyses to adjudicate farmers’ financial status, extort money, commit identity theft, etc. As a result, compelling data security schemes have become crucial for secure precision farming, where only legitimate users are required to access the agricultural data outsourced to the cloud. This article presents a certificateless signcryption scheme with proxy re-encryption (CLS-PRE) for secure access control in PA. An in-depth security analysis proves that the CLS-PRE scheme is secure in the Random Oracle Model. Detailed performance evaluation also shows that the scheme can reduce the time required to signcrypt and unsigncrypt messages and lower communication overhead.

The Named Data Networking (NDN) architecture is a futuristic internet infrastructure that aims to deliver content efficiently. However, NDN is faced with the challenge of ensuring the privacy of both content and names. Traditional solutions have focused on encrypting and signing content before injecting the resultant ciphertext into the NDN platform to provide confidentiality and integrity. However, these solutions fail to protect content name privacy in critical applications such as the military and healthcare. To address this challenge, we propose Privacy-Preserving Data Sharing on Named Data Networking (PRIDN), which employs a combination of proxy re-encryption and symmetric mechanisms to secure both content and names. PRIDN offers several advantages over existing solutions. Firstly, it eliminates the need for subscribers to communicate with content publishers for decryption keys, reducing communication overhead and ensuring that content publishers do not need to be online all the time to respond to key generation requests. Second, the proxy re-encryption mechanism prevents replication of ciphertexts, thus avoiding multiple instances of the same content in the network. Lastly, PRIDN also protects sensitive information in content names, preventing user profiling and censorship. Simulation results from ndnSIM and MIRACL libraries demonstrate that PRIDN reduces content retrieval time on NDN. A crypto-verification tool, Verifpal, shows that the proposed protocols are secure for real-world deployment.

The distribution of personal health records (PHRs) via a cloud server is a promising platform as it reduces the cost of data maintenance. Nevertheless, the cloud server is semi-trusted and can expose the patients’ PHRs to unauthorized third parties for financial gains or compromise the query result. Therefore, ensuring the integrity of the query results and privacy of PHRs as well as realizing fine-grained access control are critical key issues when PHRs are shared via cloud computing. Hence, we propose new personal health records sharing scheme with verifiable data integrity based on B+ tree data structure and attribute-based signcryption scheme to achieve data privacy, query result integrity, unforgeability, blind keyword search, and fine-grained access control.

The challenges of population management as urban density increase globally have compelled researchers and developers to consider more efficient means of managing resources in cities. Consequently, the smart city concept has emerged as a response to addressing the challenge of optimal resource utilization in urban centers. However, with digital technologies proliferating as key components of the solution, it is necessary to develop a digital identity solution for all components of the smart city environment. For completeness, the solution must encompass all entities, including physical and intangible assets, processes, and most importantly, its residents. Consequently, a unified, distributed data integration and efficient analysis platform is required: the digital city operating system. In this article, we focus on a key component of digital city management in the form of secure identification of individual residents. We collect user attributes and securely transmit them to other system components for verification. Upon successful completion of the verification process, a digital identity is created for the applying resident and the set of transactions leading to the ID creation are stored in the blockchain. Our system is secure and can serve as the basis for the development of a digital infrastructure for smart city management.

Key-policy attribute-based encryption (KP-ABE) is the cryptographic primitive which enables fine grained access control while still providing end-to-end encryption. Although traditional encryption schemes can provide end-to-end encryption, users have to either share the same decryption keys or the data have to be stored in multiple instances which are encrypted with different keys. Both of these options are undesirable. However, KP-ABE can provide less key overhead compared to the traditional encryption schemes. While there are a lot of KP-ABE schemes, none of them simultaneously supports multiuse of attributes, adaptive security, monotone span programs, and static security assumption. Hence, we propose a fully secure KP-ABE scheme for monotone span programs in prime-order group. This scheme uses selective security proof techniques to obtain the requisite ingredients for full security proof. This strengthens the correlation between selective and full security models and enables the transition of the best qualities in selective security models to fully secure systems. The security proof is based on decisional linear assumption and three-party Diffie–Hellman assumption.