ICT in telemedicine: conquering privacy and security issues in health care services

Advancement in telecommunication combined with improved information technology infrastructures has opened up new dimensions in e-health environment. Such technologies make readily available to access, store, manipulate and replicate medical information and images. These technologies help reduced the time and effort in diagnoses and treatment at lower cost. However, protection and authentication of such medical information and images are now becoming increasingly important in telemedicine environment, where images are readily distributed over electronic networks. Intruders/hackers may gain access to confidential information and possible alter or even delete such vital records. The ultimate success of telemedicine demands an effective technology as well as privacy and security of records should be main concern. This paper explores recent identified privacy and security issues that affect telemedicine. Featuring threats on security and authentication of medical records, and proposing digital watermarking as a technology to curb authentication issues in telemedicine is highlighted

Secure Data Distribution using Secret Splitting over Cloud

Developments are important to ride the unavoidable tide of progress. A large portion of undertakings are endeavoring to lessen their processing cost through the method of virtualization. This interest of diminishing the computing cost has induced the development of Cloud Computing. Cloud computing provides set of services to the customers over the network on rented basis which can be scaled up or down as per customers requirements. Typically cloud computing administrations are conveyed by an outsider supplier who possesses the foundation. In this paper we are focusing on secure data distribution over cloud using secret splitting. This will help us to achieve data confidentiality, integrit

Client-side encryption and key management: enforcing data confidentiality in the cloud.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2016.Cloud computing brings flexible, scalable and cost effective services. This is a computing paradigm whose services are driven by the concept of virtualization and multi-tenancy. These concepts bring various attractive benefits to the cloud. Among the benefits is reduction in capital costs, pay-per-use model, enormous storage capacity etc. However, there are overwhelming concerns over data confidentiality on the cloud. These concerns arise from various attacks that are directed towards compromising data confidentiality in virtual machines (VMs). The attacks may include inter-VM and VM sprawls. Moreover, weaknesses or lack of data encryption make such attacks to thrive. Hence, this dissertation presents a novel client-side cryptosystem derived from evolutionary computing concepts. The proposed solution makes use of chaotic random noise to generate a fitness function. The fitness function is used to generate strong symmetric keys. The strength of the encryption key is derived from the chaotic and randomness properties of the input noise. Such properties increase the strength of the key without necessarily increasing its length. However, having the strongest key does not guarantee confidentiality if the key management system is flawed. For example, encryption has little value if key management processes are not vigorously enforced. Hence, one of the challenges of cloud-based encryption is key management. Therefore, this dissertation also makes an attempt to address the prevalent key management problem. It uses a counter propagation neural network (CPNN) to perform key provision and revocation. Neural networks are used to design ciphers. Using both supervised and unsupervised machine learning processes, the solution incorporates a CPNN to learn a crypto key. Using this technique there is no need for users to store or retain a key which could be compromised. Furthermore, in a multi-tenant and distributed environment such as the cloud, data can be shared among multiple cloud users or even systems. Based on Shamir's secret sharing algorithm, this research proposes a secret sharing scheme to ensure a seamless and convenient sharing environment. The proposed solution is implemented on a live openNebula cloud infrastructure to demonstrate and illustrate is practicability

Restoration Data Storage in Multi-cloud Storage Services

Multi-Cloud Storage infers the utilization of various appropriated stockpiling organizations using a singular web interface rather than the defaults given by the circulated stockpiling shippers in a single heterogeneous plan. This Multi-Cloud accumulating model empowers customers to store cut mixed data in various cloud drives. Right now, offers assistance for various appropriated stockpiling organizations using the single interface as opposed to using single circulated stockpiling organizations. Cloud security objective basically focuses on issues that relate to information insurance and security parts of dispersed processing. Likewise, the data in clients' information can be spilled e.g., by methods for malignant insiders, indirect accesses, pay off and pressure. This latest data accumulating organization and data control model focus on vindictive insider's passageway on set aside data, affirmation from malignant archives, removal of united dissemination of data storing and clearing of out of date records or downloaded records once in a while. Data owner doesn't generally need to worry over the destiny of the data set aside in the Multi-Cloud server may be removed or ruined. The other is entrance control of data. The exploratory results exhibit that the suggested show is suitable for essential authority process for the data owners in the better choice of multi-disseminated capacity advantage for sharing their information securely

Secure Blockchain Transactions for Electronic Health Records based on an Improved Attribute-Based Signature Scheme (IASS)

Electronic Health Records (EHRs) are entirely controlled by hospitals, not patients, making it difficult to obtain medical advice from individual hospitals. Patients need to keep tabs on their health details and take back control of their medical data. The rapid development of blockchain technology has facilitated large-scale healthcare, including medical records and patient-related data. The technology provides comprehensive and immutable patient records and free access to electronic medical records for providers and treatment portals. To ensure the validity of the blockchain-connected EHR, the Improved Attribute-Based Signature Scheme (IASS) has considerable powers, allowing patients to approve messages based on attributes but not validated. In addition, it avoids the problem of having multiple authorities without a single or central source of trust for generating and distributing patient public/private keys and fits into the blockchain model for distributed data storage. By sharing a secret, pseudo-random activity seed between authorities, the protocol resists collusive attacks by corrupt officials. The technology provides patients with a comprehensive, immutable record and free access to their EHR from providers and treatment portals. To ensure the validity of blockchain-connected EHRs, propose an attribute-based multi-authority signature scheme that authorizes messages based on their attributes without revealing any information

FinBTech: Blockchain-Based Video and Voice Authentication System for Enhanced Security in Financial Transactions Utilizing FaceNet512 and Gaussian Mixture Models

In the digital age, it is crucial to make sure that financial transactions are as secure and reliable as possible. This abstract offers a ground-breaking method that combines smart contracts, blockchain technology, FaceNet512 for improved face recognition, and Gaussian Mixture Models (GMM) for speech authentication to create a system for video and audio verification that is unmatched. Smart contracts and the immutable ledger of the blockchain are combined to offer a safe and open environment for financial transactions. FaceNet512 and GMM offer multi-factor biometric authentication simultaneously, enhancing security to new heights. By combining cutting-edge technology, this system offers a strong defense against identity theft and illegal access, establishing a new benchmark for safe financial transactions

Efficient data uncertainty management for health industrial internet of things using machine learning

[EN] In modern technologies, the industrial internet of things (IIoT) has gained rapid growth in the fields of medical, transportation, and engineering. It consists of a self-governing configuration and cooperated with sensors to collect, process, and analyze the processes of a real-time system. In the medical system, healthcare IIoT (HIIoT) provides analytics of a huge amount of data and offers low-cost storage systems with the collaboration of cloud systems for the monitoring of patient information. However, it faces certain connectivity, nodes failure, and rapid data delivery challenges in the development of e-health systems. Therefore, to address such concerns, this paper presents an efficient data uncertainty management model for HIIoT using machine learning (EDM-ML) with declining nodes prone and data irregularity. Its aim is to increase the efficacy for the collection and processing of real-time data along with smart functionality against anonymous nodes. It developed an algorithm for improving the health services against disruption of network status and overheads. Also, the multi-objective function decreases the uncertainty in the management of medical data. Furthermore, it expects the routing decisions using a machine learning-based algorithm and increases the uniformity in health operations by balancing the network resources and trust distribution. Finally, it deals with a security algorithm and established control methods to protect the distributed data in the exposed health industry. Extensive simulations are performed, and their results reveal the significant performance of the proposed model in the context of uncertainty and intelligence than benchmark algorithms.This research is supported by Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh Saudi Arabia. Authors are thankful for the support.Haseeb, K.; Saba, T.; Rehman, A.; Ahmed, I.; Lloret, J. (2021). Efficient data uncertainty management for health industrial internet of things using machine learning. International Journal of Communication Systems. 34(16):1-14. https://doi.org/10.1002/dac.4948114341