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With technological advancements and increasing globalization, cloud computing has emerged as one of the most favoured environments for managing and maintaining data, information, and services worldwide. However, this widespread use of cloud computing necessitates addressing various security challenges and implementing adequate provisions for ensuring data confidentiality and privacy. This paper proposes a modified model to address the above challenges. An improved access control system utilizing attribute-based encryption (ABE) has been proposed for secure cloud access. In this system, the message is broadcasted by the owner, and only precise users possessing definite attributes and constraints are authorized to access shared data. The encryption is carried out on the basis of attributes of the users, and each user's secret share is generated based on their given identity information. The distributed hash table (DHT) is used to distribute ciphertext share and decryption key. The authorized users can receive the disseminated ciphertext only by utilizing their secret shares and the required attributes. The proposed model has been experimentally evaluated and compared with existing state-of-the-art systems, demonstrating its effectiveness and superiority. Additionally, the model incorporates extra features such as a time limit for data availability, biometrics-based identity verification, and resistance to various attacks. [ABSTRACT FROM AUTHOR]

In the last couple of decades, Attribute-Based Encryption (ABE) has been a promising encryption technique to realize fine-grained access control over encrypted data. ABE has appealing functionalities such as (i) access control through encryption and (ii) encrypting a message to a group of recipients without knowing their actual identities. However, the existing state-of-the-art ABEs are based on number-theoretic hardness assumptions. These designs are not secure against attacks by quantum algorithms such as Shor algorithm. Moreover, existing Post-Quantum Cryptography (PQC)-based ABEs fail to provide long-term security. Therefore, there is a need for quantum secure ABE that can withstand quantum attacks and provides long-term security. In this work, for the first time, we introduce the notion of a quantum-secure ABE (qABE) framework that preserves the classical ABE's functionalities and resists quantum attacks. Next, we provide a generic construction of qABE which is able to transform any existing ABE into qABE scheme. Thereafter, we illustrate a concrete construction of a quantum ABE based on our generic transformation qABE and the Waters' ciphertext-policy ABE scheme. [ABSTRACT FROM AUTHOR]

Fog computing is a revolutionary technology that, by expanding the cloud computing paradigm to the network edge, brings a significant achievement in the resource-constrained IoT applications in intelligent environments. However, security matters still challenge the extensive deployment of fog computing infrastructure. Ciphertext policy attribute-based encryption prepares a solution for data sharing and security preservation issues in fog-enhanced intelligent environments. Nevertheless, the lack of an effective mechanism to moderate the execution time of CP-ABE schemes due to the diversity of attributes used in secret key and access structure, as well as ensuring data security, practically restricts the deployment of such schemes. In this regard, a collaborative semantic model, including an outsourced CP-ABE scheme with the attribute revocation ability, together with an impressive AES algorithm relying on an ensemble learning system, was proposed in this study. The ensemble learning model uses multiple classifiers, including the GMDH, SVM, and KNN, to specify attributes corresponding to CP-ABE. The Dragonfly algorithm with a semantic leveling method generates outstanding and practical feature subsets. The experimental results on five smart building datasets indicate that the recommended model performs more accurately than existing methods. Also, the encryption, decryption, and attribute revocation execution time are significantly modified with the average time of 1.95, 2.11, and 14.64 ms, respectively, compared to existing works and conducted the scheme's security. [ABSTRACT FROM AUTHOR]

Most current broadcast encryption with optimal parameters is limited to Nick's class 1 (NC1) circuits and does not support polynomial‐depth circuits (P‐depth circuits), making it difficult to provide flexible access control in broadcast channels among vast user groups. To address this problem, we propose a ciphertext‐policy attribute–based encryption (CP‐ABE) that supports P‐depth circuits on lattices, achieving fully collusion resistance with randomization via the matrix tensors, thereby, making it impossible for unauthorized users to get any details about the plaintext even though they join forces and reducing the security to the evasive learning with errors (evasive LWE). By using matrix tensor–based randomization and evasive LWE, we achieve a new optimal broadcast encryption scheme based on lattice specifically designed to support P‐depth circuits. Since the matrices we choose as tensors have a low‐norm block diagonal structure, the use of evasive LWE is sufficient to ensure security for our scheme. Compared with similar studies, it not only avoids being involved with low‐norm matrices that restrict the system to NC1 circuits, but also eliminates the need for an additional assumption of the unproven tensor LWE. In addition, the use of matrix tensors further expands the dimensionality, which in turn enables the encryption of bit strings rather than a single bit, significantly reducing ciphertext expansion. Meanwhile, the CP‐ABE that we use to achieve the broadcast encryption scheme has a more compact ciphertext with a parameter size of O(m2 · d). [ABSTRACT FROM AUTHOR]

Since the advent of networked systems, fuzzy graph theory has surfaced as a fertile paradigm for handling uncertainties and ambiguities. Among the different modes of handling challenges created by the uncertainties and ambiguities of current networked systems, integrating fuzzy graph theory with cryptography has emerged as the most promising approach. In this regard, this review paper elaborates on potentially studying fuzzy graph-based cryptographic techniques, application perspectives, and future research directions. Since the expressive power of fuzzy graphs allows the cryptographic schemes to handle imprecise information and to enhance security in many domains, several domains have benefited, such as image encryption, key management, and attribute-based encryption. The paper analyzes in depth the research landscape, mainly by focusing on the varied techniques used, such as fuzzy logic for key generation and fuzzy attribute representation for access control policies. A comparison with performance metrics unveils the trade-offs and advantages of different fuzzy graph-based approaches in efficiency, security strength, and computational overhead. Additionally, the survey explores the security applications of fuzzy graph-based cryptography and underpins potential development for secure communication in wireless sensor networks, privacy-preserving data mining, fine-grained access control in cloud computing, and blockchain security. Some challenges and research directions, such as the standardization of fuzzy logic operators, algorithmic optimization, integration with emerging technologies, and exploitation of post-quantum cryptography applications, are also brought out. This review will thus bring insight into this interdisciplinary domain and stimulate further research for the design of more robust, adaptive, and secure cryptographic systems in the wake of rising complexities and uncertainties. [ABSTRACT FROM AUTHOR]

With the development of the data-sharing system in recent years, financial management systems and their privacy have sparked great interest. Existing financial data-sharing systems store metadata, which include a hash value and database index on the blockchain, and store high-capacity actual data in the center database. However, current data-sharing systems largely depend on centralized systems, which are susceptible to distributed denial-of-service (DDoS) attacks and present a centralized attack vector. Furthermore, storing data in a local center database has a high risk of information disclosure and tampering. In this paper, we propose the ChainMaker Privacy Computing (CPC) system, a new decentralized data-sharing system for secure financial data, to solve this problem. It provides a series of financial data information and a data structure rather than actual data on the blockchain to protect the privacy of data. We utilize a smart contract to establish a trusted platform for the local database to obtain encrypted data. We design a resource catalog to provide a trusted environment of data usage in the privacy computing system that is visible for members on the blockchain. Based on cipher-policy attribute-based encryption (CP-ABE), We design a CPC-CP-ABE algorithm to enable fine-grained access control through attribute based encryption. Finally, We propose an efficient scheme that allows authenticated data-sharing systems to perform Boolean searches on encrypted data information. The results of experiment show that the CPC system can finish trusted data sharing to all organizations on the blockchain. [ABSTRACT FROM AUTHOR]

Verifiable outsourced attribute-based encryption (VO-ABE) enables one-to-many data sharing and fine-grained access control under lower trust, making it suitable for cloud or edge systems involving resource-constrained devices. There is no fully outsourced and fully verifiable attribute-based encryption scheme or key-policy VO-ABE scheme. Moreover, the previous VO-ABE schemes require multiple rounds of interaction or high verification costs to support verifiable outsourced key generation and verifiable outsourced encryption. To address these issues, in this paper, we propose an effective key-policy fully outsourced and fully verifiable attribute-based encryption scheme supporting verifiable outsourced key generation, encryption, and decryption simultaneously. We formally define two new properties: outsourced key generation verifiability and outsourced encryption verifiability. Analysis and simulation show that the proposed scheme performs well and is practical. All local computational overheads of the proposed scheme are constant and do not increase with the number of attributes or the complexity of access structures. [ABSTRACT FROM AUTHOR]

Recently, Zhao et al. proposed a revocable lattice-based ciphertext-policy attributebased encryption scheme and claimed that it could resist quantum attacks and support finegrained access control. However, there is a security flaw in their scheme. Any user may change an access policy and decrypt the ciphertext corresponding to a chosen access policy. This paper presents how the attack works. [ABSTRACT FROM AUTHOR]

Federated learning enables data owners in the Internet of Things (IoT) to collaborate in training models without sharing private data, creating new business opportunities for building a data market. However, in practical operation, there are still some problems with federated learning applications. Blockchain has the characteristics of decentralization, distribution, and security. The blockchain-enabled federated learning further improve the security and performance of model training, while also expanding the application scope of federated learning. Blockchain has natural financial attributes that help establish a federated learning data market. However, the data of federated learning tasks may be distributed across a large number of resource-constrained IoT devices, which have different computing, communication, and storage resources, and the data quality of each device may also vary. Therefore, how to effectively select the clients with the data required for federated learning task is a research hotspot. In this paper, a two-stage client selection scheme for blockchain-enabled federated learning is proposed, which first selects clients that satisfy federated learning task through attribute-based encryption, protecting the attribute privacy of clients. Then blockchain nodes select some clients for local model aggregation by proximal policy optimization algorithm. Experiments show that the model performance of our two-stage client selection scheme is higher than that of other client selection algorithms when some clients are offline and the data quality is poor. [ABSTRACT FROM AUTHOR]

With the rapid development of medical information technology, the widespread adoption and application of electronic medical data have prompted more and more healthcare institutions to choose to store medical data in cloud servers to facilitate easier sharing. Attribute-based encryption is utilized for sharing electronic medical data to achieve fine-grained access control. However, storing access policies in plaintext can easily expose user privacy. Additionally, during the data sharing process, placing data retrieval in the cloud prevents secure and reliable searches. To address these issues, this paper proposes a blockchain-based privacy preserving and keyword-searchable scheme for medical data sharing(BPPKS). Access policies are transformed into vector-matrix form, concealing attributes within access policies to prevent the leakage of authorized user privacy information. Leveraging blockchain's transparency, tamper-resistance, and integrity verification features, smart contracts are used for retrieval and verification, enabling secure and reliable data searches while ensuring the integrity of medical data. Simultaneously, some complex decryption operations are delegated to the cloud servers, reducing the decryption load for users to a constant level. Finally, security analysis demonstrates that this scheme can withstand adaptive chosen keyword attacks (IND-CKA), and performance evaluations show higher efficiency in computation and storage aspects. [ABSTRACT FROM AUTHOR]

Federated learning enables training healthcare diagnostic models across multiple decentralized devices containing local private health data samples, without transferring data to a central server, providing privacy‐preserving services for healthcare professionals. However, for a model of a specific field, some medical data from non‐target participants may be included in model training, compromising model accuracy. Moreover, diagnostic queries for healthcare models stored in cloud servers may result in the leakage of the privacy of healthcare participants and the parameters of models. Furthermore, the records of model searching and usage could be tracked causing privacy disclosure risk. To address these issues, we propose a blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for the diagnostic model federated learning in the Internet of Medical Things (BSAEM‐FL). We first adopt fine‐grained model trainer participation policies for federated learning, using the attribute‐based encryption (ABE) mechanism, to realize model accuracy and local data privacy. Then, We employ searchable encryption technology for model training and usage to protect the security of models stored in the cloud server. Blockchain is utilized to implement distributed healthcare models' keyword‐based search and model users' attribute‐based authentication. Lastly, we transfer most of the computational overhead of user terminals in model searching and decryption to edge nodes, achieving lightweight computation of IoMT terminals. The security analysis proves the security of the proposed healthcare scheme. The performance evaluation indicates our scheme is of better feasibility, efficiency, and decentralization. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Location-based services (LBSs) rely on geographic information to offer location-based functionality to users, providing comfort to people’s lives but also being accompanied by privacy leaks. Many of the central verification methods proposed in recent years are not satisfactory, as they pose a significant risk to the user’s privacy. Blockchain technology provides a decentralized, distributed and manipulative solution, providing innovation in data sharing and management, as the technology is employed to enhance data privacy protection through various mechanisms, such as hashing and encryption secure transactions and protect user identities. In this paper, we propose a blockchain-based approach to secure location and time data privacy. We present a novel access control system that leverages location-based policies and multi-authority attributes. This approach allows users to remain anonymous while granting access based on their dynamic location and time. By combining multiple private chains, users’ transaction records can be distributed, which can provide users with stronger on-site privacy protection without affecting service quality. Comparative simulation analysis with state-of-the-art methods using benchmark performance metrics shows that our proposed approach provides improved security, efficiency, and transparency. We use the real or random (ROR) model to verify the security of session keys and mutual authentication of the proposed approach. Finally, theoretical security analysis and informal and formal security verifications show that our approach is secure against multiple attacks. We use the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool to simulate the resistance of the proposed approach to security attacks. Our evaluation is based on the use of the JPBC (Java Pairing-Based Cryptography) library. Furthermore, validation and performance discussion show that decrypting our approach is preferable. [ABSTRACT FROM AUTHOR]

To address fine-grained access control, secure sharing, and encrypted key updates in a one-to-many model, a multi-keyword attribute-based searchable encryption scheme with proxy re-encryption for cloud storage was proposed. The access tree was enhanced with node information for fine-grained control over ciphertext read and write permissions. The keyword encryption process was optimized for trapdoor indistinguishability and restricted user search capabilities. Re-encryption updated ciphertext and keys, preventing malicious access by revoked users. A blockchain-based verification algorithm was designed to detect tampering of third-party data. The DBDH and DDH hard problems proved the scheme’s keyword ciphertext security and trapdoor security. Experiments show the proposed scheme secures data and keys during key generation, trapdoor, and index generation, and correctness verification. It also demonstrates higher efficiency in time overhead, ensuring privacy and key safety while maintaining high efficiency.

Abstract At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security.

As UWSN have received attention, a need for an efficient and secure communication protocol has arisen to overcome the low device performance, node failure, and high propagation latency. However, existing works are either specialized for one-to-one communication or cannot satisfy low latency constraints. Therefore, this work proposes a secure communication protocol with fine-grained access control for UWSN that support secure and efficient one-to-many communication and considers potential internal attackers for high security level. Specifically, we adopt lightweight ABE to achieve fine-grained access control at low cost, and introduce outsourced decryption to further alleviate the computational load of underwater sensors.

The Military Internet of Things (MIoT) has emerged as a new research area in military intelligence. The MIoT frequently has to constitute a federation-capable IoT environment when the military needs to interact with other institutions and organizations or carry out joint missions as part of a coalition such as in NATO. One of the main challenges of deploying the MIoT in such an environment is to acquire, analyze, and merge vast amounts of data from many different IoT devices and disseminate them in a secure, reliable, and context-dependent manner. This challenge is one of the main challenges in a federated environment and forms the basis for establishing trusting relationships and secure communication between IoT devices belonging to different partners. In this work, we focus on the problem of fulfillment of the data-centric security paradigm, i.e., ensuring the secure management of data along the path from its origin to the recipients and implementing fine-grained access control mechanisms. This problem can be solved using innovative solutions such as applying attribute-based encryption (ABE). In this work, we present a comprehensive solution for secure data dissemination in a federated MIoT environment, enabling the use of distributed registry technology (Hyperledger Fabric), a message broker (Apache Kafka), and data processing microservices implemented using the Kafka Streams API library. We designed and implemented ABE cryptography data access control methods using a combination of pairings-based elliptic curve cryptography and lightweight cryptography and confirmed their suitability for the federations of military networks. Experimental studies indicate that the proposed cryptographic scheme is viable for the number of attributes typically assumed to be used in battlefield networks, offering a good trade-off between security and performance for modern cryptographic applications. [ABSTRACT FROM AUTHOR]

At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security. [ABSTRACT FROM AUTHOR]

The blockchain-based Electronic Medical Record (EMR) data storage system encounters challenges which are data leakage, insufficient access control, and low retrieval efficiency. In response to these issues, this paper proposes a lightweight searchable encryption and access control system for sharing EMR data securely. The lightweight searchable encryption scheme which is achieved by integrating the trapdoor verification phase with the keyword retrieval phase is constructed using BGG13 + and MP12. User access control is implemented through a Boolean circuit to replace arithmetic operations and thus improve arithmetic efficiency. Since logic operations can reduce the computation time, this paper uses Boolean circuits for user rights verification implementation. In addition, this paper adopts the Bloom filter as the system index to enhance the efficiency of block-chain data retrieval. According to the simulation results, there is a performance advantage of this system over similar systems. [ABSTRACT FROM AUTHOR]

Although the copyright protection schemes supported by blockchain have significantly changed traditional copyright data management, there are still some data security challenges that cannot be ignored, especially the secure access and controllable management of copyright data. Quantum computing attacks also pose a threat to its security. Targeting these issues, we design and propose a blockchain copyright protection scheme based on attribute-based encryption (ABE). In this scheme, the security advantages of blockchain technology are utilized to ensure the authenticity and integrity of copyright data. Based on lattice cryptography and the decision ring learning with errors (R-LWE) problem, a new ABE algorithm that supports searchable ciphertext and policy updates is designed. Then, we introduce it into the blockchain copyright protection scheme, which enables secure access to copyright data and fine-grained control. In addition, the lattice cryptography can strengthen this scheme against quantum attacks. Through security analysis, our scheme can prove to be secure against adaptive chosen keyword attacks, selective chosen plaintext attacks, and adaptive chosen policy attacks in the random oracle model. More importantly, the comparison analysis and experimental results show that our proposed approach has lower computation costs and storage costs. Therefore, our scheme has better security and performance in copyright protection. [ABSTRACT FROM AUTHOR]

Ensuring data confidentiality is a critical requirement for modern security systems globally. Despite the implementation of various access-control policies to enhance system security, significant threats persist due to insecure and inadequate access management. To address this, Multi-Party Authorization (MPA) systems employ multiple authorities for authorization and authentication, utilizing blockchain technology to store and access data securely, ensuring immutable and trusted audit trails. In this work, we propose a hybrid key-generation approach called the Identity and Attribute-Based Honey Encryption (IABHE) Algorithm combined with Deep Spiking Neural Network (DSNN) denoted by IABHE+DSNN for secure data sharing in a multi-party blockchain-based system. This approach incorporates various entities and multiple security functionalities to ensure data security. The data-sharing process involves several steps: initialization, authentication, initial registration, data protection, validation, and data sharing. Data protection is executed within the MapReduce framework, with data encryption performed using IABHE and key generation managed by DSNN. Experimental results demonstrate that the proposed IABHE+DSNN approach achieves a decryption time of 10.786 s, an encryption time of 15.765 s, and a key complexity of 0.887, outperforming existing methods. [ABSTRACT FROM AUTHOR]

To ensure both data security and usability in cloud environments simultaneously, Searchable Encryption (SE) emerges as a crucial technique. Through the utilization of Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the Ciphertext-Policy AttributeBased Keyword Search (CP-ABKS) scheme accomplishes keyword-based retrieval and fine-grained access control concurrently. However, existing CP-ABKS schemes with a single attribute authority entail costly user certificate verification and secret key distribution, leading to a performance bottleneck in distributed cloud systems. Thus, this study introduces a secure Multi-authority CPABKS (MABKS) system to overcome these challenges and alleviate the computation and storage burden on resource-limited devices within cloud systems. Furthermore, the MABKS system extends its functionality to support malicious attribute authority tracing and attribute updates. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The emergence of cloud computing has made it possible to access applications and data from anywhere. Due to its flexibility, efficiency, and resource sharing capabilities, cloud computing has been rapidly applied to various industries and fields. Fine-grained data access control, privacy protection, and privilege control in cloud computing have become hot research issues in both academia and industry. To address these issues, this paper proposes a data and privilege hybrid access control scheme based on SM9 attribute encryption. In scenarios that require encryption of a large amount of hierarchical data, this scheme is more flexible and efficient than traditional attribute-based encryption schemes, the anonymity of it can reduce the leakage of user ' s privacy, and hierarchical privilege control makes it possible to manage the operation privilege of cloud data more finely. The security analysis and simulation experiments show that, the proposed scheme is secure under decisional bilinear Diffie-Hellman assumption, and it is efficient in encryption and decryption. [ABSTRACT FROM AUTHOR]

A chameleon hash function is a type of hash function that involves a trapdoor to help find collisions, i.e., it allows the rewriting of a message without modifying the hash. For some applications, it is important to have the feature of revoking the rewriting privilege of the trapdoor holder. In this paper, using lattice-based hard problems that are considered quantum-safe, we first introduce a lattice-based chameleon hash with an ephemeral trapdoor (CHET)\left({\mathsf{CHET}}) and then a revocable attribute-based encryption (RABE{\mathsf{RABE}}) scheme that is adaptively indistinguishable. We also give security analyses of our schemes and compare our RABE{\mathsf{RABE}} scheme to two relevant schemes proposed recently. Furthermore, we combine our CHET{\mathsf{CHET}} and RABE{\mathsf{RABE}} to design a new revocable policy-based chameleon hash.

Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records.

Cloud computing technology offers flexible and expedient services that carry a variety of profits for both societies as well as individuals. De-duplication techniques were developed to minimize redundant data in the cloud storage. But, one of the main challenges of cloud storage is data deduplication with secure data storage.To overcome the issue, we propose Boneh Goh Nissim Bilinear Attribute-based Optimal Cache Oblivious (BGNBA-OCO) access control and secure de-duplication for data storage in cloud computing in this paper. The proposed method achieves fne-grained access control with low computation consumption. We design Boneh Goh Nissim Privacy Preserving Revocable Attribute-based Encryption that reinforces attribute revocation and averts the discharge of sensitive information. Furthermore, we utilize Optimal Cache Oblivious algorithm to prevent disclosure of access patterns to hide the access patterns in cloud storage via rand pattern matching. We support updating both encrypted data and access control policies to minimize communication and computation overhead of data duplication and encryption processes concurrently. We perform secure data sharing to achieve higher data confidentiality and integrity. Finally, we conducted the extensive experiments in cloud and the results illustrated that our proposed BGNBA-OCO method is more efficient than related works. [ABSTRACT FROM AUTHOR]

Fog computing provides a suitable development for real-time processing in the Internet of Things (IoT). Attribute-based encryption (ABE) is the main method to control data access in fog computing. A residue number system (RNS) can speed up multiplication and exponential operations by converting very large integers to small integers. This paper proposes a fine-grained lightweight access control scheme using ABE modified with RNS properties (RNS-ABE) with fog computing. Decryption is implemented with the Chinese remainder theorem (CRT), and a new access structure based on the CRT secret sharing scheme is also introduced. Security of the proposed scheme proved based on RNS properties and the complicated problem of factoring a very large integer into its large prime factors, like RSA encryption. The time cost comparison shows that the total encryption and decryption time of our scheme is more efficient than the lightweight schemes with the underlying operation of bilinear pairing. [ABSTRACT FROM AUTHOR]

Ciphertext-policy attribute-based encryption (CP-ABE) is widely employed for secure data sharing and access control. However, its dependence on a single authority introduces security and performance challenges. Despite the existence of multi-authority CPABE approaches, persistent issues such as single points of failure and high computation cost on the user side remain. This study proposes a novel solution named blockchain-based and decentralized attribute-based encryption (BDAE) for data sharing. BDAE enhances traditional scheme by integrating blockchain and distributed key generation technology. The scheme employs an (n, t) threshold secret sharing algorithm, coupled with the Pedersen verifiable secret sharing method, for attribute key generation. This combination ensures key credibility, facilitates joint attribute management, and addresses single bottleneck and key verification issues. Integrated into a blockchain system, the scheme utilizes smart contracts for fine-grained access control and outsourced computing. Blockchain's decentralization and access logs make data sharing tamper-resistant and auditable. Moreover, simulation comparisons demonstrate that the scheme effectively reduces decryption overhead on the user side, meeting practical application requirements. [ABSTRACT FROM AUTHOR]