ABSTRACT Blockchain sharding has emerged as a promising solution to address the scalability challenges of modern blockchain systems, yet in practice, existing sharding systems still suffer from serious performance bottlenecks. In recent work, LB‐Chain proposes a framework for load balancing by dynamically migrating hot accounts, which significantly improves throughput and latency. However, the approach still suffers from the computational overhead associated with frequent full ordering, which limits its efficiency in large‐scale systems. To address this issue, this paper proposes an enhanced sharding framework, DH‐Chain, which improves computational efficiency by introducing heap sorting optimization, dynamically managing the load of sharding and accounts, and avoiding full‐volume sorting during each round of migration. The experimental results show that DH‐Chain achieves 3.2% higher throughput than LB‐Chain and 11.4% higher than random allocation, approaching the theoretical upper bound of ideal allocation. By leveraging heap‐based sorting and two‐phase commit protocols, DH‐Chain ensures atomicity and security while reducing computational overhead. The framework effectively balances shard loads, maintaining consistent performance across varying transaction loads and demonstrating robust scalability.

Cryptocurrency-based digital payments are integral to modern financial systems, forming dynamic transaction networks shaped by user interactions. However, accurately predicting potential future links within these networks is critical for understanding transaction evolution and mitigating risks such as fraud. Traditional methods, often based on static graph embeddings, struggle to capture the temporal dependencies and incomplete information inherent in evolving transaction graphs. This article proposes an advanced multi-graph gated-long short-term memory (MGGLSTM) framework for link prediction in cryptocurrency transaction networks. The MGGLSTM framework integrates a multigraph attention neural networks (GATConv) with a MGGLSTM module to capture both temporal dependencies and complex interaction patterns. It further incorporates a missing information prediction mechanism that refines node embeddings through adaptive corrections, thereby improving robustness against incomplete data. A reachability-guided random walk strategy is employed to extract node and edge features, preserving temporal structures across time-sliced transaction graphs. For link prediction, edge embeddings are generated from the concatenation of node representations and classified using a multi-layer perceptron (MLP)-based edge classifier. Experiments on real-world benchmark cryptocurrency transaction networks from both Bitcoin and Ethereum demonstrate that MGGLSTM significantly outperforms baseline methods, achieving higher area under curve (AUC), Precision, Recall, and F1-scores, thereby validating its effectiveness in dynamic link prediction.

Blockchain fraud detection confronts escalating challenges in Ethereum ecosystems where evolving fraud patterns result in billions of dollars in annual losses, yet existing ap proaches fail to adapt without catastrophic forgetting while pro viding no theoretical guarantees for financial system deployment. This paper presents AHGT-DFD, a framework integrating four ML techniques automated pattern discovery through Dirichlet process priors, hierarchical variational learning, heterogeneous graph transformers, and continual learning mechanisms into a theoretically grounded system. We provide rigorous theoret ical foundations including PAC-learning bounds guaranteeing generalization performance within 6.21% of empirical error with 95% confidence, polynomial-time convergence analysis, and certified robustness against adversarial perturbations. Compre hensive evaluation on real-world Ethereum datasets demonstrates 4.62 percentage point F1-score improvement for Ponzi detection (95.58% vs. 92.15% best baseline) and 4.94 percentage point improvement for phishing detection (97.41% vs. 94.47% best baseline). The framework maintains 94.2% performance reten tion on evolving patterns compared to 78.6% for conventional approaches, while supporting real-time processing with sub-15ms inference latency suitable for production blockchain security systems. This integration with theoretical guarantees provides a foundational advance for dependable fraud detection in adver sarial financial environments.

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.

As data-driven applications and user demands grow, managing content delivery in Named Data Networking (NDN) has become more challenging. Traditional caching methods struggle to scale in dynamic and decentralized environments where content popularity changes and collaboration among routers is needed. This paper introduces a decentralized collaborative caching framework for NDN, combining Multi-Agent Deep Reinforcement Learning (MADRL) and blockchain technology. MADRL enables routers to autonomously adjust caching strategies based on local states and interactions with neighboring routers, improving cache hit rates and reducing retrieval costs. Blockchain technology ensures fair and transparent rewards through a cryptocurrency-based token system, incentivizing collaboration and minimizing free-riding risks. The framework also integrates Delegated Proof of Stake (DPoS) for efficient, secure validation of caching actions. Simulations demonstrate that the approach significantly enhances caching efficiency, reduces latency, and improves scalability, addressing the challenges of dynamic decentralized environments.

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.

The rapid growth of blockchain networks has introduced unprecedented challenges in detecting anomalous activities within complex transaction graphs. While existing approaches struggle with multi-edge scenarios and temporal dependencies, we present SentinelGNN, a novel graph neural network architecture that achieves state-of-the-art performance in detecting five types of blockchain anomalies: point, contextual, collective, temporal, and structural. Our key technical innovations include: (1) a temporal-aware edge sampling mechanism that effectively handles multiple transaction edges while preserving critical temporal information, (2) a dual-stream architecture that separately processes structural and temporal patterns through specialized neural pathways, and (3) an adaptive gating mechanism that dynamically fuses topological and feature information based on their relative importance. Through extensive experimentation on a large-scale Ethereum dataset containing 6.08M nodes and 38.90M edges, SentinelGNN achieves ROC-AUC scores of 0.980 ± 0.01 and PR-AUC scores of 0.975 ± 0.01, outperforming traditional graph neural networks by significant margins (27% on point anomalies, 25% on contextual anomalies, and 22% on collective anomalies). The model demonstrates particular strength in handling temporal pattern deviations while maintaining computational efficiency, processing 100,000 transactions per second. Comprehensive ablation studies validate the effectiveness of each component, with the temporal-aware sampling providing a 15% improvement in pattern recognition accuracy. Beyond blockchain security, SentinelGNN shows promise in financial fraud detection and supply chain monitoring, offering a scalable foundation for securing decentralized systems.

In the Internet of Things (IoT) and Big Data era, large-scale data analysis necessitates rapid and efficient machine learning techniques. Traditional distributed learning methods often rely on a central server and an online privacy model, which poses challenges for scalability and data privacy. We propose a decentralized High-level Proof-of-Work (HPoW) algorithm to address these issues, leveraging blockchain technology to develop a comprehensive predictive model. Our approach ensures participant data privacy through the implementation of a differential privacy scheme and establishes a secure environment against Byzantine attacks. Furthermore, the training of numerous machine learning models in cloud computing environments can lead to data anomalies, adversely affecting resource allocation and overall system performance. We introduce a cloud computing incentive mechanism designed specifically for distributed machine learning frameworks to mitigate these challenges. We present novel methodologies and provide mathematical proof that our blockchain-based approach significantly enhances the establishment of a Nash equilibrium among participating entities. Finally, we present the machine learning service on the blockchain with the cloud service aiding through in-depth experiments to show how well it works and to conclude on a transparent Nash equilibrium value with an incentive value of 14.3 Gwei after a successful session, highlighting our proposed system’s practical viability and performance benefits.

Recently, the adoption of IoT (Internet of Things) and Blockchain has become a hot topic, particularly in areas such as education and industry. IoT involves billions of devices connected worldwide and the management of these devices is largely based on centralized systems. Thus, users will have no choice but to trust these systems. Blockchain (BC), a distributed immutable time-stamped ledger that provides decentralization, immutability, and high security can help solve some of the problems inherent in the IoT landscape. However, integrating blockchain and IoT is not trivial; it comes with some difficulties such as scalability problems, high computational costs, and overheads among others. Therefore, this paper presents a scalable and lightweight Blockchain IoT service system using multi-edge servers that reduces computational overhead by 42% and improves transaction throughput to 658 tps, representing a 2.12-3.76× improvement compared to existing approaches. Our architecture uniquely combines blockchain, group signature, and message authentication code to ensure dependable auditing of users’ access records, anonymous authentication of smart home members, and effective verification of the home management system while maintaining a memory footprint of 4.2 MB, 60-70% smaller than conventional blockchain implementations. Additionally, our solution achieves 21-46% lower communication overhead (240 bytes per transaction) and 55-73% reduced latency (850 ms), demonstrating significant improvements across all performance metrics. The distributed nature of our multi-edge server approach eliminates single points of failure and enables a transaction processing capability that scales linearly with network growth, addressing key limitations in current blockchain-IoT integrations.

Non-fungible token (NFT) markets present a dual analytical challenge: integrating heterogeneous data modalities (high-dimensional visual features and discrete transaction sequences) while preserving privacy for sensitive wallet addresses and trading strategies. Current approaches analyze visual attributes or transaction patterns in isolation, missing critical value drivers from cross-modal interactions. Meanwhile, existing multimodal techniques lack formal privacy guarantees, exposing participants to inference attacks. This article introduces PrivaMod, a privacy-preserving Bayesian framework that addresses these limitations through uncertainty-aware multimodal fusion. Our approach implements precision-weighted Bayesian fusion that dynamically adjusts modality contributions based on quantified uncertainty levels, while integrating Rényi Differential Privacy throughout the pipeline via calibrated noise injection and adaptive gradient clipping. Evaluated on 167,492 CryptoPunk transactions, PrivaMod achieves a market efficiency score of 0.874 and R 2 of 0.912, outperforming existing methods by 13.4% through superior cross-modal integration while maintaining strong privacy guarantees ( \(\varepsilon\) = 0.08, \(\delta\) = 1e-5) with membership inference attack success rates near random guessing (53.4%). The system demonstrates that privacy-preserving techniques can enhance rather than compromise analytical performance, establishing a foundation for responsible market analysis. To ensure reproducibility, we release our code, preprocessed datasets, and model checkpoints with detailed documentation and scripts to replicate all experiments. PrivaMod is available at https://github.com/kvjunior/PrivaMod/blob/main/README.md .

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.

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.

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 increasing adoption of Electric Vehicle (EV) systems necessitates the development of an Energy Market structure that facilitates peer-to-peer energy sharing among multiple EVs and entities while ensuring a self-regulating pricing mechanism. Real-time State of Charge (SoC) estimation is critical to meeting the dynamic energy demands of EV systems. In this study, we propose a blockchain-based automated market maker (AMM) that utilizes constant function products to establish an effective self-regulating pricing system for EV energy market prices. Our unique State of Charge estimation system leverages blockchain-based oracles to efficiently handle requests and monitor EV-oriented energy markets. This enables precise monitoring of battery states and achieves improved SoC values through the interior point method. Experimentation on a blockchain network reveals cost-effective energy regulation within EV systems and enhanced SoC estimation predictability within Energy Markets. All contracts undergo rigorous testing and are deployed at a gas cost of$2.1913742~x 10^{7}$Wei. Our approach demonstrates high efficiency, for all designed protocols, affirming the efficacy of our proposal. By implementing our blockchain-based AMM and State of Charge estimation system, we ensure transparent and self-regulated energy distribution and pricing within EV Markets, fostering the advancement of autonomous EV systems.

The Covid-19 pandemic ushered in multiple paradigms of personal health data sharing with particular emphasis on Person-to-Institution sharing and Institution-toInstitution sharing. While the data aggregated by technology companies and health authorities was instrumental in the development of vaccines and ultimately flattening the curve of infection rates, egregious abuses of privacy occurred. In many instances acceptable guarantees of appropriate utility for the data were not made available. Personal health data sharing for the containment of infections with privacy limitations present a classic case of collaboration among mutually distrustful entities. In this regard the blockchain network and attendant protocols for data integrity, transaction transmission and provenance can prove useful. Thus, in this paper we present a blockchain-based method for disease surveillance in a smart environment where smart contracts are deployed to monitor public locations instead of individuals. The data aggregated is analysed and tagged with a lifetime commensurate with the time for infection. Once the data utility period has elapsed the monitored data are removed from the active surveillance pool and the entities involved can be notified. Such a method of continual surveillance protects privacy by shifting the emphasis from individuals to locations. Experimental data suggests this method is efficient and can be implemented on top of existing disease surveillance strategies for later pandemics.

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.

Owing to the vast volume of information gathered by computers, data protection and security has become a problem for organizations with the enormous rise in data transmission. Due to many advantages that cloud service providers provide, mainly economic benefits, several data owners outsource their data to cloud repositories. However, data owners do not have full ownership of the data after their data are outsourced. Thus, external data management systems are implemented to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data. Selective encryption aims at supporting selective and private access to outsourced data. However, the combination of this approach and indexing techniques cause confidentiality violations. In this article, a blockchain-based approach to data access is presented by implementing smart contracts over data access. These executable scripts bind users by stating access policies on the data. Furthermore, we provide a system, where users can offload their computational capabilities due to limitations in their computations. Our systems' computational capabilities outperform that of when users do the computation on their own. The results show a practical approach to data access management using blockchain technology.

In the era of big data, fast and effective machine learning algorithms are urgently required for large-scale data analysis. Data is usually created from several parts and stored in a geographically distributed manner, which has stimulated research in the field of distributed machine learning. The traditional master-level distributed learning algorithm involves the use of a trusted central server and focuses on the online privacy model. On the contrary, the specific linear learning model and security issues are not well understood in this column. We built a decentralized advanced-Proof-of-Work (aPoW) algorithm specifically for learning a general predictive model over the blockchain. In aPoW, we establish the data privacy of the differential privacy based schemes to protect each party and propose a secure domain against potential Byzantine attacks at a reduced rate. We explored a technical module in newsprint to consider a universal learning model (linear or non-linear) to provide a secure, confidential decentralized machine learning system called deepLearning Chain. Finally, we introduce deepLearning Chain on blockchain through comprehensive experiments, demonstrate its performance and effectiveness.

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.

The goal of intelligent transport systems (ITSs) is to enhance the network performance of vehicular ad hoc networks (VANETs). Even though it presents new opportunities to the Internet of Vehicles (IoV) environment, there are some security concerns including the need to establish trust among the connected peers. The fifth-generation (5G) communication system, which provides reliable and low-latency communication services, is seen as the technology to cater for the challenges in VANETs. The incorporation of software-defined networks (SDNs) also ensures an effective network management. However, there should be monitoring and reporting services provided in the IoV. Blockchain, which has decentralization, transparency, and immutability as some of its properties, is designed to ensure trust in networking platforms. In that regard, this article analyzes the combination of blockchain and SDN for the effective operation of the VANET systems in 5G and fog computing paradigms. With managerial responsibilities shared between the blockchain and the SDN, it helps to relieve the pressure off the controller due to the ubiquitous processing that occurs. A trust-based model that curbs malicious activities in the network is also presented. The simulation results substantially guarantee an efficient network performance, while also ensuring that there is trust among the entities.

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.

With the vast increase in data transmission due to a large number of information collected by devices, data management, and security has been a challenge for organizations. Many data owners (DOs) outsource their data to cloud repositories due to several economic advantages cloud service providers present. However, DOs, after their data are outsourced, do not have complete control of the data, and therefore, external systems are incorporated to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data but prove to be deficient in providing suitable solutions to the problem. In this article, we propose a secure fine-grain access control system for outsourced data, which supports read and write operations to the data. We make use of an attribute-based encryption (ABE) scheme, which is regarded as a suitable scheme to achieve access control for security and privacy (confidentiality) of outsourced data. This article considers different categories of data users, and make provisions for distinct access roles and permissible actions on the outsourced data with dynamic and efficient policy updates to the corresponding ciphertext in cloud repositories. We adopt blockchain technologies to enhance traceability and visibility to enable control over outsourced data by a DO. The security analysis presented demonstrates that the security properties of the system are not compromised. Results based on extensive experiments illustrate the efficiency and scalability of our system.

Health information exchanges have been popular for some time with their advantages known and widely researched. In spite of their utility in increasing provider efficiency and decreasing administrative costs, one challenge that has persisted is the data owners inability to control data after transmission. The lack of technical mechanisms to effectively control patients' health data in the network significantly affects participation of health and medical institutions while perpetrating the silo-based data management that locks value and potential inherent in the data. This not only affects researchers due to the lack of data for research and analysis but the quality of life of patients.We present a blockchain-supported architectural framework for secure control of personal data in a health information exchange by pairing user-generated acceptable use policies with smart contracts. We highlight the merits of our system, its user-centric focus and also show experimental results along with directions for extending our work. The framework introduces minimal risk to data by architecting a mechanism for controlling data after sharing. In adopting our framework, health service providers can deliver a stronger assurance for data management than is possible with current systems.