Research on the Sensitive Data Protection Method Based on Game Theory Algorithm

2021 ◽  
pp. 238-248
Author(s):  
Yunfeng Zou ◽  
Pengfei Yu ◽  
Chao Shan ◽  
Meng Wu
Keyword(s):  
Game Theory ◽  
Data Protection ◽  
Sensitive Data ◽  
Protection Method

Fast Selective Encryption Methods for Bitmap Images

2015 ◽  
Vol 6 (3) ◽  
pp. 51-69 ◽  
Author(s):  
Han Qiu ◽  
Gerard Memmi
Keyword(s):  
Data Protection ◽  
General Purpose ◽  
Use Cases ◽  
Process Unit ◽  
Selective Encryption ◽  
Quality Of Data ◽  
Protection Method ◽  
Reconstruction Quality ◽  
Image Protection

The authors are interested in image protection within resource environments offered by commodity computers such as desktops, laptops, tablets, or even smartphones. Additionally, the authors have in mind use cases where a large amount images are to be protected. Traditional encryption is not fast enough for such environments and such use cases. The authors derived a new solution by parallelizing selective encryption and using available GPU (Graphic Process Unit) acceleration. Progress obtained in terms of performance allows considering selective encryption as a general purpose solution for the use cases considered. After presenting related works, a ‘first level' of protection is described and a new ‘strong level' of protection method is introduced. Different architecture designs and implementation choices are extensively discussed, considering various criteria: performance indeed, but also image reconstruction quality and quality of data protection.

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.

A Method for User Data Protection Based on Game Theory in Cloud Computing

2017 ◽  
Author(s):  
Xiaohui Li ◽  
Feng Liu ◽  
Hongxing Liang
Keyword(s):  
Game Theory ◽  
Cloud Computing ◽  
Data Protection ◽  
User Data

scholarly journals A Secure Storage System for Sensitive Data Protection Based on Mobile Virtualization

2015 ◽  
Vol 11 (2) ◽  
pp. 929380 ◽  
Author(s):  
Su-Wan Park ◽  
JaeDeok Lim ◽  
Jeong Nyeo Kim
Keyword(s):  
Data Protection ◽  
Storage System ◽  
Sensitive Data ◽  
Secure Storage

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.

scholarly journals Packet fragmentation as data protection method in automated systems

2020 ◽  
Vol 862 ◽  
pp. 052027
Author(s):  
D E Gorokhov ◽  
V V Ryabokon ◽  
A A Kuzkin ◽  
V S Sherbakov ◽  
M A Kutsakin
Keyword(s):  
Data Protection ◽  
Automated Systems ◽  
Protection Method ◽  
Packet Fragmentation
Sign in / Sign up

Export Citation Format

Share Document