Engineering Graphical Captcha and AES Crypto Hash Functions for Secure Online Authentication

Kheshaifaty, Nafisah; Gutub, Adnan

doi:10.36909/jer.13761

Cited by 37 publications

(12 citation statements)

References 26 publications

(33 reference statements)

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“…As pointed out in [29], the protection of personal data from malicious entities and systems must be implemented at different levels such as processing, storage, communication and device levels. Although digital transformation is significant for the healthcare sector, susceptibility to cyber attacks is a fundamental challenge [30], [31], [32], [33].The general public trepidation towards e-healthcare is mainly due to privacy and security issues [30], [34]. Whereas privacy is concerned with the protection of the collected patients' data that can uniquely identify them, security is concerned with the restriction and authorization during access to this personal information [35].…”

Section: Figurementioning

confidence: 99%

A survey of healthcare sector digitization strategies: Vulnerabilities, countermeasures and opportunities

Nyakina¹,

Taher²

2023

World J. Adv. Eng. Technol. Sci.

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The adoption of electronic healthcare in hospital environment can potentially reduce costs and improve the quality of life of the patients. However, numerous security and privacy issues arise when sensitive patient data is shared among multiple devices and users. Owing to its vulnerable nature, electronic health records seem to be more attractive to attackers compared to other forms of records such as financial transactions. Consequently, the patient data collected at the sensors, transmitted across communication channels and residing in hospital servers is susceptible to various threats. The goal of this paper was to carry out a survey of the electronic healthcare environment and attempt to understand the various weaknesses that can be exploited. This is followed by some descriptions of the various preventive mechanisms as well as the noted gaps. Therefore, numerous recommendations are given that are deemed fit for enhanced security and privacy posture in electronic healthcare domain.

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Section: Figurementioning

confidence: 99%

A survey of healthcare sector digitization strategies: Vulnerabilities, countermeasures and opportunities

Nyakina¹,

Taher²

2023

World J. Adv. Eng. Technol. Sci.

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“…It uses PUF Physical Unclonable Function (PUF) and Elliptic Curve Cryptography (ECC). The PUF provides secure authentication [65], [66], [67] between the server and the tag, while ECC does the encryption of PUF information. The result is a lightweight authentication scheme.…”

Section: Security In Big Data Healthcarementioning

confidence: 99%

A survey on techniques, methods and security approaches in big data healthcare

Odera¹

2023

Global J. Eng. Technol. Adv.

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A huge percentage of people especially in developed countries spend a good chunk of their wealth in managing their health conditions. In order to adequately administer healthcare, governments and various organizations have embraced advanced technology for automating the health industry. In recent past, electronic health records have largely been managed by Enterprise Resource Planning and legacy systems. Big data framework steadily emerge as the underlying technology in healthcare, which offers solutions that limits capacity of others systems in terms of storage and reporting. Automation through cloud services supported by storage of structured and unstructured health data in heterogeneous environment has improved service delivery, efficiency, medication, diagnosis, reporting and storage in healthcare. The argument support the idea that big data healthcare still face information security concern, for instance patient image sharing, authentication of patient, botnet, correlation attacks, man-in-the-middle, Distributed Denial of Service (DDoS), blockchain payment gateway, time complexities of algorithms, despite numerous studies conducted by scholars in security management for big data in smart healthcare. Some security technique include digital image encryption, steganography, biometrics, rule-based policy, prescriptive analysis, blockchain contact tracing, cloud security, MapReduce, machine-learning algorithms, anonymizations among others. However, most of these security solutions and analysis performed on structured and semi-structured data as opposed to unstructured data. This may affect the output of medical reporting of patients’ condition particularly on wearable devices and other examinations such as computerized tomography (CT) Scans among others. A major concern is how to identify inherent security vulnerabilities in big healthcare, which generate images for transmission and storage. Therefore, this paper conducted a comparative survey of solutions that specifically safeguards structured and unstructured data using systems that run on big data frameworks. The literature highlights several security advancements in cryptography, machine learning, anonymization and protocols. Most of these security frameworks lacks implementation evidence. A number of studies did not provide comprehensive performance metrics (accuracy, error, recall, precision) of the models besides using a single algorithm without validated justification. Therefore, a critique on the contribution, performance and areas of improvements discussed and summarized in the paper.

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“…Unfortunately, the proposed method cannot be extended or generalized to cover further genomic sequences other than protein testing of this paperwork, since the HT-NGH method has been applied on protein only, similar to pairwise sequence alignment based on transition probability between two consecutive pairs of residues [61] as well as rigid region pairwise sequence alignments [62]. The work can further involve engineering Hash functions [63] combined with partitioning of watermarking via counting-based secret sharing [64] as for semicomplete text reliability [65] aiming innovative researches. Accordingly, the proposed computational protein alignment utilizing hyper threading multi-core sharing technology method needs to be extended precisely for other genomic sequences, which are still open research applicable to give independent supplementary remarks.…”

Section: Possible Extension To Genomic Sequencesmentioning

confidence: 99%

Efficient computation of Hash Hirschberg protein alignment utilizing hyper threading multi‐core sharing technology

Abu-Hashem

Gutub

2021

CAAI Trans on Intel Tech

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Due to current technology enhancement, molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge data. Therefore, Multi-processing CPUs technology can be used including physical and logical processors (Hyper Threading) to significantly increase the performance of computations. Accordingly, sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal alignments. This research used the Hash Table-NGram-Hirschberg (HT-NGH) algorithm to represent this pairwise alignment utilizing hashing capabilities. The authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise alignment. The proposed parallel hyper threading method targeted the transformation of the HT-NGH on the datasets decomposition for sequence level efficient utilization within the processing units, that is, reducing idle processing unit situations. The authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking performance of 24.8% average speed up to 34.4% as the highest boosting rate. The benefit of this work improvement is shown preserving acceptable accuracy, that is, reaching 2.08, 2.88, and 3.87 boost-up as well as the efficiency of 1.04, 0.96, and 0.97, using 2, 3, and 4 cores, respectively, as attractive remarkable results.

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Engineering Graphical Captcha and AES Crypto Hash Functions for Secure Online Authentication

Cited by 37 publications

References 26 publications

A survey of healthcare sector digitization strategies: Vulnerabilities, countermeasures and opportunities

A survey of healthcare sector digitization strategies: Vulnerabilities, countermeasures and opportunities

A survey on techniques, methods and security approaches in big data healthcare

Efficient computation of Hash Hirschberg protein alignment utilizing hyper threading multi‐core sharing technology

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