Cryptanalysis on Secure ECC based Mutual Authentication Protocol for Cloud-Assisted TMIS

The creation of TMIS (Telecare Medical Information System) makes it simpler for patients to receive healthcare services and opens up options for seeking medical attention and storing medical records with access control. With Wireless Medical Sensor Network and cloud-based architecture, TMIS gives the chance to patients to collect their physical health information from medical sensors and also upload this information to the cloud through their mobile devices. The communication is held through internet connectivity, therefore security and privacy are the main motive aspects of a secure cloud-assisted TMIS. However, because very sensitive data is transmitted between patients and doctors through the cloud server, thus security protection is important for this system. Recently, Kumar et al designed a mutual authentication protocol for cloud-assisted TMIS based on ECC [2]. In this paper, we revisited this scheme and traced out that their scheme has some significant pitfalls like health report revelation attack, and report confidentiality. In this study, we will provide the cryptanalysis of the scheme developed by Kumar et al

INFORMATION SECURITY: A STUDY ON BIOMETRIC SECURITY SOLUTIONS FOR TELECARE MEDICAL INFORMATION SYSTEMS

This exploratory study provides a means for evaluating and rating Telecare medical information systems in order to provide a more effective security solution. This analysis of existing solutions was conducted via an in-depth study of Telecare security. This is a proposition for current biometric technologies as a new means for secure communication of private information over public channels. Specifically, this research was done in order to provide a means for businesses to evaluate prospective technologies from a 3 dimensional view in order to make am accurate decision on any given biometric security technology. Through identifying key aspects of what makes a security solution the most effective in minimizing risk of a patient’s confidential data being exposed we were then able to create a 3 dimensional rubric to see not only from a business view but also the users such as the patients and doctors that use Telecare medical information systems every day. Finally, we also need to understand the implications of biometric solutions from a technological standpoint

Privacy protection for telecare medicine information systems using a chaotic map-based three-factor authenticated key agreement scheme

Telecare Medicine Information Systems (TMIS) provides flexible and convenient e-health care. However the medical records transmitted in TMIS are exposed to unsecured public networks, so TMIS are more vulnerable to various types of security threats and attacks. To provide privacy protection for TMIS, a secure and efficient authenticated key agreement scheme is urgently needed to protect the sensitive medical data. Recently, Mishra et al. proposed a biometrics-based authenticated key agreement scheme for TMIS by using hash function and nonce, they claimed that their scheme could eliminate the security weaknesses of Yan et al.’s scheme and provide dynamic identity protection and user anonymity. In this paper, however, we demonstrate that Mishra et al.’s scheme suffers from replay attacks, man-in-the-middle attacks and fails to provide perfect forward secrecy. To overcome the weaknesses of Mishra et al.’s scheme, we then propose a three-factor authenticated key agreement scheme to enable the patient enjoy the remote healthcare services via TMIS with privacy protection. The chaotic map-based cryptography is employed in the proposed scheme to achieve a delicate balance of security and performance. Security analysis demonstrates that the proposed scheme resists various attacks and provides several attractive security properties. Performance evaluation shows that the proposed scheme increases efficiency in comparison with other related schemes

Security for networked smart healthcare systems: A systematic review

Background and Objectives Smart healthcare systems use technologies such as wearable devices, Internet of Medical Things and mobile internet technologies to dynamically access health information, connect patients to health professionals and health institutions, and to actively manage and respond intelligently to the medical ecosystem's needs. However, smart healthcare systems are affected by many challenges in their implementation and maintenance. Key among these are ensuring the security and privacy of patient health information. To address this challenge, several mitigation measures have been proposed and some have been implemented. Techniques that have been used include data encryption and biometric access. In addition, blockchain is an emerging security technology that is expected to address the security issues due to its distributed and decentralized architecture which is similar to that of smart healthcare systems. This study reviewed articles that identified security requirements and risks, proposed potential solutions, and explained the effectiveness of these solutions in addressing security problems in smart healthcare systems. Methods This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and was framed using the Problem, Intervention, Comparator, and Outcome (PICO) approach to investigate and analyse the concepts of interest. However, the comparator is not applicable because this review focuses on the security measures available and in this case no comparable solutions were considered since the concept of smart healthcare systems is an emerging one and there are therefore, no existing security solutions that have been used before. The search strategy involved the identification of studies from several databases including the Cumulative Index of Nursing and Allied Health Literature (CINAL), Scopus, PubMed, Web of Science, Medline, Excerpta Medical database (EMBASE), Ebscohost and the Cochrane Library for articles that focused on the security for smart healthcare systems. The selection process involved removing duplicate studies, and excluding studies after reading the titles, abstracts, and full texts. Studies whose records could not be retrieved using a predefined selection criterion for inclusion and exclusion were excluded. The remaining articles were then screened for eligibility. A data extraction form was used to capture details of the screened studies after reading the full text. Of the searched databases, only three yielded results when the search strategy was applied, i.e., Scopus, Web of science and Medline, giving a total of 1742 articles. 436 duplicate studies were removed. Of the remaining articles, 801 were excluded after reading the title, after which 342 after were excluded after reading the abstract, leaving 163, of which 4 studies could not be retrieved. 159 articles were therefore screened for eligibility after reading the full text. Of these, 14 studies were included for detailed review using the formulated research questions and the PICO framework. Each of the 14 included articles presented a description of a smart healthcare system and identified the security requirements, risks and solutions to mitigate the risks. Each article also summarized the effectiveness of the proposed security solution. Results The key security requirements reported were data confidentiality, integrity and availability of data within the system, with authorisation and authentication used to support these key security requirements. The identified security risks include loss of data confidentiality due to eavesdropping in wireless communication mediums, authentication vulnerabilities in user devices and storage servers, data fabrication and message modification attacks during transmission as well as while the data is at rest in databases and other storage devices. The proposed mitigation measures included the use of biometric accessing devices; data encryption for protecting the confidentiality and integrity of data; blockchain technology to address confidentiality, integrity, and availability of data; network slicing techniques to provide isolation of patient health data in 5G mobile systems; and multi-factor authentication when accessing IoT devices, servers, and other components of the smart healthcare systems. The effectiveness of the proposed solutions was demonstrated through their ability to provide a high level of data security in smart healthcare systems. For example, proposed encryption algorithms demonstrated better energy efficiency, and improved operational speed; reduced computational overhead, better scalability, efficiency in data processing, and better ease of deployment. Conclusion This systematic review has shown that the use of blockchain technology, biometrics (fingerprints), data encryption techniques, multifactor authentication and network slicing in the case of 5G smart healthcare systems has the potential to alleviate possible security risks in smart healthcare systems. The benefits of these solutions include a high level of security and privacy for Electronic Health Records (EHRs) systems; improved speed of data transaction without the need for a decentralized third party, enabled by the use of blockchain. However, the proposed solutions do not address data protection in cases where an intruder has already accessed the system. This may be potential avenues for further research and inquiry