369 research outputs found
On Evaluating Commercial Cloud Services: A Systematic Review
Background: Cloud Computing is increasingly booming in industry with many
competing providers and services. Accordingly, evaluation of commercial Cloud
services is necessary. However, the existing evaluation studies are relatively
chaotic. There exists tremendous confusion and gap between practices and theory
about Cloud services evaluation. Aim: To facilitate relieving the
aforementioned chaos, this work aims to synthesize the existing evaluation
implementations to outline the state-of-the-practice and also identify research
opportunities in Cloud services evaluation. Method: Based on a conceptual
evaluation model comprising six steps, the Systematic Literature Review (SLR)
method was employed to collect relevant evidence to investigate the Cloud
services evaluation step by step. Results: This SLR identified 82 relevant
evaluation studies. The overall data collected from these studies essentially
represent the current practical landscape of implementing Cloud services
evaluation, and in turn can be reused to facilitate future evaluation work.
Conclusions: Evaluation of commercial Cloud services has become a world-wide
research topic. Some of the findings of this SLR identify several research gaps
in the area of Cloud services evaluation (e.g., the Elasticity and Security
evaluation of commercial Cloud services could be a long-term challenge), while
some other findings suggest the trend of applying commercial Cloud services
(e.g., compared with PaaS, IaaS seems more suitable for customers and is
particularly important in industry). This SLR study itself also confirms some
previous experiences and reveals new Evidence-Based Software Engineering (EBSE)
lessons
Optical TEMPEST
Research on optical TEMPEST has moved forward since 2002 when the first pair
of papers on the subject emerged independently and from widely separated
locations in the world within a week of each other. Since that time,
vulnerabilities have evolved along with systems, and several new threat vectors
have consequently appeared. Although the supply chain ecosystem of Ethernet has
reduced the vulnerability of billions of devices through use of standardised
PHY solutions, other recent trends including the Internet of Things (IoT) in
both industrial settings and the general population, High Frequency Trading
(HFT) in the financial sector, the European General Data Protection Regulation
(GDPR), and inexpensive drones have made it relevant again for consideration in
the design of new products for privacy. One of the general principles of
security is that vulnerabilities, once fixed, sometimes do not stay that way.Comment: 6 pages, 2 figures; accepted to the International Symposium and
Exhibition on Electromagnetic Compatibility (EMC Europe 2018), 27--30 August
2018, in Amsterdam, The Netherland
Technical Privacy Metrics: a Systematic Survey
The file attached to this record is the author's final peer reviewed versionThe goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature makes an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over eighty privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement
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