scholarly journals A Survey on Security Mechanisms for NoC-based Many-Core SoCs

2021 ◽  
Vol 16 (2) ◽  
pp. 1-15
Author(s):  
Luciano Lores Caimi ◽  
Rafael Faccenda ◽  
Fernando Gehm Moraes
Keyword(s):  
Data Protection ◽  
Resource Sharing ◽  
Design Requirement ◽  
Sensitive Data ◽  
Research Opportunities ◽  
Critical Design ◽  
Many Core ◽  
Safe Access ◽  
Application Execution

The adoption of many-cores systems introduces the concern for data protection as a critical design requirement due to the resource sharing and the simultaneous executions of several applications on the platform. A secure application that processes sensitive data may have its security harmed by a malicious process. The literature contains several proposals to protect many-cores against attacks, focusing on the protection of the application execution or the access to shared memories. However, there is a gap to be fulfilled: a solution covering the entire application lifetime, including its admission, execution, and peripheral's access. This survey discusses three security-related issues: the secure admission of applications, the prevention of resource sharing during their execution, and the safe access to external devices. This survey concludes with an evaluation of the studied methods, pointing out directions and research opportunities.

scholarly journals A Data Protection Framework for Learning Analytics

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Andrew Nicholas Cormack
Keyword(s):  
Data Protection ◽  
Learning Analytics ◽  
Personal Data ◽  
Informed Choice ◽  
Ethical Framework ◽  
Early Interventions ◽  
Sensitive Data ◽  
Analysis Pattern ◽  
Open Questions ◽  
Automated Processing

Most studies on the use of digital student data adopt an ethical framework derived from human-studies research, based on the informed consent of the experimental subject. However consent gives universities little guidance on the use of learning analytics as a routine part of educational provision: which purposes are legitimate and which analyses involve an unacceptable risk of harm. Obtaining consent when students join a course will not give them meaningful control over their personal data three or more years later. Relying on consent may exclude those most likely to benefit from early interventions. This paper proposes an alternative framework based on European Data Protection law. Separating the processes of analysis (pattern-finding) and intervention (pattern-matching) gives students and staff continuing protection from inadvertent harm during data analysis; students have a fully informed choice whether or not to accept individual interventions; organisations obtain clear guidance: how to conduct analysis, which analyses should not proceed, and when and how interventions should be offered. The framework provides formal support for practices that are already being adopted and helps with several open questions in learning analytics, including its application to small groups and alumni, automated processing and privacy-sensitive data.

scholarly journals eHealth Cloud Security Challenges: A Survey

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yazan Al-Issa ◽  
Mohammad Ashraf Ottom ◽  
Ahmed Tamrawi
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Energy Savings ◽  
Service Providers ◽  
Healthcare Providers ◽  
Cloud Service ◽  
Cloud Security ◽  
Security And Privacy ◽  
Healthcare Industry ◽  
Sensitive Data

Cloud computing is a promising technology that is expected to transform the healthcare industry. Cloud computing has many benefits like flexibility, cost and energy savings, resource sharing, and fast deployment. In this paper, we study the use of cloud computing in the healthcare industry and different cloud security and privacy challenges. The centralization of data on the cloud raises many security and privacy concerns for individuals and healthcare providers. This centralization of data (1) provides attackers with one-stop honey-pot to steal data and intercept data in-motion and (2) moves data ownership to the cloud service providers; therefore, the individuals and healthcare providers lose control over sensitive data. As a result, security, privacy, efficiency, and scalability concerns are hindering the wide adoption of the cloud technology. In this work, we found that the state-of-the art solutions address only a subset of those concerns. Thus, there is an immediate need for a holistic solution that balances all the contradicting requirements.

Internet-Oriented Environment for Scientific Computing Resource Sharing

2012 ◽  
Vol 241-244 ◽  
pp. 2953-2956
Author(s):  
Shu Fang Zhang ◽  
Jun Han ◽  
Fei Jiang
Keyword(s):  
Resource Sharing ◽  
High Performance ◽  
Scientific Computing ◽  
Distributed Applications ◽  
Computing Environment ◽  
Resource Monitoring ◽  
Computing Resource ◽  
System Resource ◽  
Measurement Scheduling ◽  
Application Execution

In this paper, we introduced a scientific computing environment for Internet-Oriented computing resource sharing, abbreviate ISCEs, which is a high-performance computing environment that allows users to write and evaluate parallel distributed applications for different hardware and software configurations using a web interface. We described the software architecture of ISCEs by emphasizing Application editor, Application Scheduling Components, and Application execution/runtime modules. ISCEs is efficient which is strongly supported by the time measurement scheduling polices. The system resource monitoring can also benefit a lot from the Application execution/runtime modules. The results obtained from performance analysis show that Scalability and Speedup of ISCEs was good.

scholarly journals Autonomia informacyjna jednostki a zgoda na przetwarzanie przez pracodawcę danych osobowych

2020 ◽  
Vol 6(161) ◽  
pp. 47-67
Author(s):  
Karol Grzybowski
Keyword(s):  
Data Protection ◽  
Personal Data ◽  
Self Determination ◽  
Sensitive Data ◽  
Personal Data Protection

By adapting the provisions of the Labour Code to EU regulations on personal data protection, the legislator has explicitly allowed employers to process personal data of employees and applicants for employment on the basis of their consent. However, the new provisions exclude the processing of data on convictions on this basis and limit the possibility of giving effective consent to the processing of sensitive data. The article attempts to analyze the solutions adopted in the context of the constitutional guarantee of informational self-determination. The author defends the thesis that the provisions of Article 221a § 1 and Article 221b § 1 of the Labour Code disproportionately interfere with an individual’s right to dispose of data concerning him or her. These provisions do not meet the criterion of the intervention’s necessity. The protective goal of the regulation, as established by the legislator, may be achieved by means of the legal instruments indicated in the article, which do not undermine the freedom aspect of the informational self-determination.

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