Two-person Cooperative Games

An exhaustive survey on security and privacy issues in Healthcare 4.0

Applications of Blockchain (BC) technology and Cyber-Physical Systems (CPS) are increasing exponentially. However, framing resilient and correct smart contracts (SCs) for these smart application is a quite challenging task because of the complexity associated with them. SC is modernizing the traditional industrial, technical, and business processes. It is self-executable, self-verifiable, and embedded into the BC that eliminates the need for trusted third-party systems, which ultimately saves administration as well as service costs. It also improves system efficiency and reduces the associated security risks. However, SCs are well encouraging the new technological reforms in Industry 4.0, but still, various security and privacy challenges need to be addressed. In this paper, a survey on SC security vulnerabilities in the software code that can be easily hacked by a malicious user or may compromise the entire BC network is presented. As per the literature, the challenges related to SC security and privacy are not explored much by the authors around the world. From the existing proposals, it has been observed that designing a complex SCs cannot mitigate its privacy and security issues. So, this paper investigates various Artificial Intelligence (AI) techniques and tools for SC privacy protection. Then, open issues and challenges for AI-based SC are analyzed. Finally, a case study of retail marketing is presented, which uses AI and SC to preserve its security and privacy.

/pdf/smart-contract-privacy-protection-using-ai-in-cyber-physical-1i4efbw1la.pdf

Smart Contract Privacy Protection Using AI in Cyber-Physical Systems: Tools, Techniques and Challenges

Aktivitet: Journal of Electronic Commerce in Organizations

Big data analytics for wireless and wired network design: A survey

Even though Automatic Speech Recognition (ASR) has matured to the point of commercial applications, high error rate in some speech recognition domains remain as one of the main impediment factors to the wide adoption of speech technology, and especially for continuous large vocabulary speech recognition applications. The persistent presence of ASR errors have intensified the need to find alternative techniques to automatically detect and correct such errors. The correction of the transcription errors is very crucial not only to improve the speech recognition accuracy, but also to avoid the propagation of the errors to the subsequent language processing modules such as machine translation. In this paper, basic principles of ASR evaluation are first summarized, and then the state of the current ASR errors detection and correction research is reviewed. We focus on emerging techniques using word error rate metric.

Automatic Speech Recognition Errors Detection and Correction: A Review.

Automatic Speech Recognition Errors Detection and Correction: A Review

Security Enhancement in Healthcare Cloud using Machine Learning

Cloud computing is one of the new and very promising trends in the IT technology field, it is subject to a lot of research and continuous advances are made. Its main objectives are to deliver different services for users, such as infrastructure, platform or software with a reasonable and more and more decreasing cost for the clients. To achieve those goals, some matters have to be addressed, mainly using the available resources in an effective way in order to improve the overall performance, while taking into consideration the security and the availability sides of the cloud. Hence, one of the most studied aspects by researchers is load balancing in cloud computing, especially for the big distributed cloud systems that deal with many clients and big amounts of data and requests. In this paper, we address the subject of load balancing in cloud computing and present a semi centralized and multi cluster architecture. This proposed approach mainly ensures a better overall performance with efficient load balancing, the continuous availability and a security aspect.

A secured load balancing architecture for cloud computing based on multiple clusters

Nowadays,﻿modern﻿healthcare﻿providers﻿create﻿massive﻿medical﻿images﻿every﻿day﻿because﻿of﻿the﻿recent﻿ progress﻿in﻿imaging﻿tools.﻿This﻿is﻿generally﻿due﻿to﻿the﻿increasing﻿number﻿of﻿patients﻿demanding﻿medical﻿ services.﻿This﻿has﻿resulted﻿in﻿a﻿continuous﻿demand﻿of﻿a﻿large﻿storage﻿space.﻿Unfortunately,﻿healthcare﻿ domains﻿still﻿use﻿local﻿data﻿centers﻿for﻿storing﻿medical﻿data﻿and﻿managing﻿business﻿processes.﻿This﻿ has﻿significant﻿negative﻿impacts﻿on﻿operating﻿costs﻿associated﻿with﻿licensing﻿fees﻿and﻿maintenance.﻿ To﻿ overcome﻿ these﻿ challenges,﻿ healthcare﻿ organizations﻿ are﻿ interested﻿ in﻿ adopting﻿ cloud﻿ storage﻿ rather﻿than﻿on-premise﻿hosted﻿solutions.﻿This﻿is﻿mainly﻿justified﻿by﻿the﻿scalability,﻿cost﻿savings﻿and﻿ availability﻿of﻿cloud﻿services.﻿The﻿primary﻿objective﻿of﻿this﻿model﻿is﻿to﻿outsource﻿data﻿and﻿delegate﻿IT﻿ computations﻿to﻿an﻿external﻿party.﻿The﻿latter﻿delivers﻿needed﻿storage﻿systems﻿via﻿the﻿Internet﻿to﻿fulfill﻿ client’s﻿demands.﻿Even﻿though﻿this﻿model﻿provides﻿significant﻿cost﻿advantages,﻿using﻿cloud﻿storage﻿ raises﻿security﻿challenges.﻿To﻿this﻿aim,﻿this﻿article﻿describes﻿several﻿solutions﻿which﻿were﻿proposed﻿ to﻿ensure﻿data﻿protection.﻿The﻿existing﻿implementations﻿suffer﻿from﻿many﻿limitations.﻿The﻿authors﻿ propose﻿a﻿framework﻿to﻿secure﻿the﻿storage﻿of﻿medical﻿images﻿over﻿cloud﻿computing.﻿In﻿this﻿regard,﻿ they﻿use﻿multi-region﻿segmentation﻿and﻿watermarking﻿techniques﻿to﻿maintain﻿both﻿confidentiality﻿ and﻿integrity.﻿In﻿addition,﻿they﻿rely﻿on﻿an﻿ABAC﻿model﻿to﻿ensure﻿access﻿control﻿to﻿cloud﻿storage.﻿This﻿ solution﻿mainly﻿includes﻿four﻿functions,﻿i.e.,﻿(1)﻿split﻿data﻿for﻿privacy﻿protection,﻿(2)﻿authentication﻿for﻿ medical﻿dataset﻿accessing,﻿(3)﻿integrity﻿checking,﻿and﻿(4)﻿access﻿control﻿to﻿enforce﻿security﻿measures.﻿ Hence,﻿the﻿proposal﻿is﻿an﻿appropriate﻿solution﻿to﻿meet﻿privacy﻿requirements. KEywORdS Cloud Computing, Medical Image, Security, Storage

Hassan Ouahmane

Papers

Automatic Speech Recognition Errors Detection and Correction: A Review.

Automatic Speech Recognition Errors Detection and Correction: A Review

Security Enhancement in Healthcare Cloud using Machine Learning

A secured load balancing architecture for cloud computing based on multiple clusters

A Framework to Secure Medical Image Storage in Cloud Computing Environment