A Secure Data Sharing Using IDSS CP-ABE in Cloud Storage

Ezhilarasi, T. P.; Kumar, Nikhil; Latchoumi, T. P.; Balayesu, N.

doi:10.1007/978-981-15-4739-3_92

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…However, in an urgent situation, the break-glass access method evades the medical file's access policy to provide emergency treatment or rescue professionals immediate access to the data. Ezhilarasi et al (2022) describes a mobile-cloud computing architecture with a lightweight data exchange mechanism. It takes mechanism from the ciphertext-policy encryption based on attributes skill used in the cloud's virtual setting.…”

Section: Related Workmentioning

confidence: 99%

Cloud-based secure data storage and access control for internet of medical things using federated learning

Bhansali

Hiran

Kothari

et al. 2022

IJPCC

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Purpose The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud. Design/methodology/approach This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design. Findings For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning. Originality/value The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

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Section: Related Workmentioning

confidence: 99%

Cloud-based secure data storage and access control for internet of medical things using federated learning

Bhansali

Hiran

Kothari

et al. 2022

IJPCC

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show abstract

“…(5) The school and hospital, as attribute authorities in the system, respectively, execute the algorithm AA.Setup to generate their own public/private key pair. (6) To prevent this process from being compromised, the CRFs of the school and hospital execute the algorithm W AA .Setup to randomize their respective public and private key pair. (7) Considering that most patients usually use resource-constrained mobile devices, some computational operations are performed in the Encrypt.off algorithm.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…Only when the attributes of the user meet the access structure of the ciphertext can the user use the private key to recover the plaintext correctly. Because users can use CP-ABE to specify flexible access structures for ciphertext, they can achieve fine-grained access control for ciphertext, so CP-ABE has been widely used in cloud computing [ 1 , 2 , 3 , 4 , 5 , 6 ]. Because the user’s private key is generated by an attribute authority, the attribute authority knows the user’s private key and can decrypt the ciphertext.…”

Section: Introductionmentioning

confidence: 99%

Online/Offline MA-CP-ABE with Cryptographic Reverse Firewalls for IoT

Bian

et al. 2023

Entropy

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Devices in the Internet of Things (IoT) usually use cloud storage and cloud computing to save storage and computing cost. Therefore, the efficient realization of one-to-many communication of data on the premise of ensuring the security of cloud storage data is a challenge. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) can not only protect the security of data in the cloud and achieve one-to-many communication but also achieve fine-grained access control for data. However, the single-authority CP-ABE faces the crisis of single point of failure. In order to improve security, the Multi-Authority CP-ABE (MA-CP-ABE) is adopted. Although there are provably-secure MA-CP-ABE schemes, Edward Snowden’s research shows that provably-secure cryptographic schemes are vulnerable to backdoor attacks, resulting in secret disclosure, and thus threatening security. In addition, ABE requires huge computational overhead in key generation, encryption and decryption, which increase with the increase in the number of attributes and the complexity of the access structure, and there are a large number of resource-constrained devices in the IoT. To mitigate this issue, we construct the Online/Offline MA-CP-ABE with Cryptographic Reverse Firewalls (OO-MA-CP-ABE-CRFs) scheme. This scheme not only uses Cryptographic Reverse Firewall (CRF) to resist backdoor attacks but also uses online/offline key generation, online/offline encryption and outsourcing encryption technology to optimize the efficiency of the MA-CP-ABE scheme with reverse firewall, reducing the storage and computing cost of users. Finally, the security of the OO-MA-CP-ABE-CRFs scheme is proved, and the experimental results indicate that the scheme is efficient and practical.

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“…In addition, the current scheduling data network service information flow storage function mainly stores the information flow data generated for the remote communication between the station and the master station but lacks the storage function for the internal data flow of the master station system [30,31]. It is difficult to combine data network information flow with scheduling key services to carry out data flow fault anomaly discrimination based on business scenarios [32,33].…”

Section: Acquisition and Structured Storage Of Data Flowmentioning

confidence: 99%

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

Luo¹,

Li²,

Yang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this area. In particular, we divide intelligent identification over power big data into data acquisition and storage processes, anomaly detection and fault discrimination processes, and fault tracing for dispatching operations during communication. A detailed survey of the solutions to the challenges in intelligent identification over power big data is then presented. Moreover, opportunities and future directions are outlined.

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A Secure Data Sharing Using IDSS CP-ABE in Cloud Storage

Cited by 11 publications

References 7 publications

Cloud-based secure data storage and access control for internet of medical things using federated learning

Cloud-based secure data storage and access control for internet of medical things using federated learning

Online/Offline MA-CP-ABE with Cryptographic Reverse Firewalls for IoT

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

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