8,675 research outputs found
Big Data Privacy Context: Literature Effects On Secure Informational Assets
This article's objective is the identification of research opportunities in
the current big data privacy domain, evaluating literature effects on secure
informational assets. Until now, no study has analyzed such relation. Its
results can foster science, technologies and businesses. To achieve these
objectives, a big data privacy Systematic Literature Review (SLR) is performed
on the main scientific peer reviewed journals in Scopus database. Bibliometrics
and text mining analysis complement the SLR. This study provides support to big
data privacy researchers on: most and least researched themes, research
novelty, most cited works and authors, themes evolution through time and many
others. In addition, TOPSIS and VIKOR ranks were developed to evaluate
literature effects versus informational assets indicators. Secure Internet
Servers (SIS) was chosen as decision criteria. Results show that big data
privacy literature is strongly focused on computational aspects. However,
individuals, societies, organizations and governments face a technological
change that has just started to be investigated, with growing concerns on law
and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions
and the only consistent country between literature and SIS adoption is the
United States. Countries in the lowest ranking positions represent future
research opportunities.Comment: 21 pages, 9 figure
Mechatronics & the cloud
Conventionally, the engineering design process has assumed that the design team is able to exercise control over all elements of the design, either directly or indirectly in the case of sub-systems through their specifications. The introduction of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) means that a design team’s ability to have control over all elements of a system is no longer the case, particularly as the actual system configuration may well be being dynamically reconfigured in real-time according to user (and vendor) context and need. Additionally, the integration of the Internet of Things with elements of Big Data means that information becomes a commodity to be autonomously traded by and between systems, again according to context and need, all of which has implications for the privacy of system users. The paper therefore considers the relationship between mechatronics and cloud-basedtechnologies in relation to issues such as the distribution of functionality and user privacy
Unleashing The Potential of Data Ecosystems: Establishing Digital Trust through Trust-Enhancing Technologies
Companies increasingly innovate data-driven business models, enabling them to create new products and services. Emerging data ecosystems provide these companies access to complementary data, offering them additional potential. This, however remains untapped, as a lack of digital trust prevents companies from sharing data within these ecosystems. By using trust-enhancing technologies, companies can establish trust; this can be explained through the theoretical lens of system trust. Using a design research approach helped us to unlock the knowledge of 21 experts and identify five technologies with the potential to solve the trust challenge: self-sovereign identities, differential privacy, fully homomorphic encryption, trusted execution environments and secure multiparty computation. We integrated these technologies into the data sharing process in data ecosystems and elaborated on their limitations and maturity. Ultimately, we derived two principles that allow for adapting our results to future technological developments: complementarity and customization
Edge AI for Internet of Energy: Challenges and Perspectives
The digital landscape of the Internet of Energy (IoE) is on the brink of a
revolutionary transformation with the integration of edge Artificial
Intelligence (AI). This comprehensive review elucidates the promise and
potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a
meticulously curated research methodology, the article delves into the myriad
of edge AI techniques specifically tailored for IoE. The myriad benefits,
spanning from reduced latency and real-time analytics to the pivotal aspects of
information security, scalability, and cost-efficiency, underscore the
indispensability of edge AI in modern IoE frameworks. As the narrative
progresses, readers are acquainted with pragmatic applications and techniques,
highlighting on-device computation, secure private inference methods, and the
avant-garde paradigms of AI training on the edge. A critical analysis follows,
offering a deep dive into the present challenges including security concerns,
computational hurdles, and standardization issues. However, as the horizon of
technology ever expands, the review culminates in a forward-looking
perspective, envisaging the future symbiosis of 5G networks, federated edge AI,
deep reinforcement learning, and more, painting a vibrant panorama of what the
future beholds. For anyone vested in the domains of IoE and AI, this review
offers both a foundation and a visionary lens, bridging the present realities
with future possibilities
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