Sensitive Data Protection of DBaaS Using OPE and FPE

Author(s):  
Kamlesh Kumar Hingwe ◽  
S. Mary Saira Bhanu
2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Andrew Nicholas Cormack

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.


2020 ◽  
Vol 6(161) ◽  
pp. 47-67
Author(s):  
Karol Grzybowski

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.


2020 ◽  
pp. 1-9
Author(s):  
Tataru Stefan Razvan ◽  
Irene Nica

Sports activities attract an impressive number of participants, manifesting themselves in a multitude of forms, in leisure or performance sports, in and out of the sports ground. In the context in which the sports industry processes a variety of personal data of athletes, including sensitive data such as information concerning health, we aim to analyse the impact of the General Regulation on the protection of personal data in sports activities. In the first part of the study we analysed the incidence of sport in daily life and the forms of organization of sports structures. Subsequently, we focused our attention in particular on the way in which the personal data of the athletes are processed, the rights they enjoy under the new European regulations and the measures that the operators should ensure for the protection of these data.


2021 ◽  
Vol 54 (1) ◽  
pp. 1-35
Author(s):  
Nikolaus Marsch ◽  
Timo Rademacher

German data protection laws all provide for provisions that allow public authorities to process personal data whenever this is ‘necessary’ for the respective authority to fulfil its tasks or, in the case of sensitive data in the meaning of art. 9 GDPR, if this is ‘absolutely necessary’. Therewith, in theory, data protection law provides for a high degree of administrative flexibility, e. g. to cope with unforeseen situations like the Coronavirus pandemic. However, these provisions, referred to in German doctrine as ‘Generalklauseln’ (general clauses or ‘catch-all’-provisions in English), are hardly used, as legal orthodoxy assumes that they are too vague to form a sufficiently clear legal basis for public purpose processing under the strict terms of the German fundamental right to informational self-determination (art. 2‍(1), 1‍(1) German Basic Law). As this orthodoxy appears to be supported by case law of the German Constitutional Court, legislators have dutifully reacted by creating a plethora of sector specific laws and provisions to enable data processing by public authorities. As a consequence, German administrative data protection law has become highly detailed and confusing, even for legal experts, therewith betraying the very purpose of legal clarity and foreseeability that scholars intended to foster by requiring ever more detailed legal bases. In our paper, we examine the reasons that underlie the German ‘ban’ on using the ‘Generalklauseln’. We conclude that the reasons do not justify the ban in general, but only in specific areas and/or processing situations such as security and criminal law. Finally, we list several arguments that do speak in favour of a more ‘daring’ approach when it comes to using the ‘Generalklauseln’ for public purpose data processing.


Author(s):  
Florian Kerschbaum

Collaborative business applications are an active field of research and an emerging practice in industry. This chapter will focus on data protection in b2b applications which offer a wide range of business models and architecture, since often equal partners are involved in the transactions. It will present three distinct applications, their business models, security requirements and the newest solutions for solving these problems. The three applications are collaborative benchmarking, fraud detection and supply chain management. Many of these applications will not be realized if no appropriate measure for protecting the collaborating parties’ data are taken. This chapter focuses on the strongest form of data protection. The business secrets are kept entirely secret from other parties (or at least to the degree possible). This also corresponds to the strongest form of privacy protection in many instances. The private information does not leave the producing system, (i.e., data protection), such that the information producer remains its sole owner. In case of B2B application, the sensitive data are usually business secrets, and not personally identifiable data as in privacy protection.


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