Privacy issues in AmI spaces

Kafeza, Eleanna; Kafeza, Irene

doi:10.1504/ijnvo.2009.027793

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Fourth, the citizens, in general, have reaped benefits from areas of AI such as police investigation, through expert systems that offer support in terms of profiling and tracking criminals based on machine-learning and neural network techniques [17], video surveillance systems of real-time accuracy [76], resorting to models to detect moving objects keeping up with environment changes [36], of dynamical sensor selection in processing the image streams of all cameras simultaneously [37], whereas ambient intelligence (AmI) spaces, in where devices, sensors, and wireless networks, combine data from diverse sources and monitor user preferences and their subsequent results on users' privacy under a regulatory privacy framework [62].…”

Section: Ai Social Applicationsmentioning

confidence: 99%

The Impact of Artificial Intelligence on Data System Security: A Literature Review

Raimundo

Rosário

2021

Sensors

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Diverse forms of artificial intelligence (AI) are at the forefront of triggering digital security innovations based on the threats that are arising in this post-COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control, and web domain, to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods, and outage management systems in an effort to understand the interplay between AI and those issues. the dependence on the emergence of AI in running industries and shaping the education, transports, and health sectors is now well known in the literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system security within the current digital society is opportune. This paper aims at identifying research trends in the field through a systematic bibliometric literature review (LRSB) of research on AI and system security. the review entails 77 articles published in the Scopus® database, presenting up-to-date knowledge on the topic. the LRSB results were synthesized across current research subthemes. Findings are presented. the originality of the paper relies on its LRSB method, together with an extant review of articles that have not been categorized so far. Implications for future research are suggested.

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Section: Ai Social Applicationsmentioning

confidence: 99%

The Impact of Artificial Intelligence on Data System Security: A Literature Review

Raimundo

Rosário

2021

Sensors

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“…Fourth, the citizens, in general, have reaped benefits from areas of AI such as police investigation, through expert systems that offer support in terms of profiling and tracking criminals based on machine-learning and neural network techniques to [17], video surveillance systems of real-time accuracy [76], resorting to models to detect moving objects keeping up with environment changes [36], of dynamical sensor selection in processing the image streams of all cameras simultaneously [37], whereas Ambient Intelligence (AmI) spaces, in where devices, sensors and wireless networks, combine data from a diverse sources and monitor user preferences and their subsequent results on users' privacy under a regulatory privacy framework [62].…”

Section: Ai Social Applicationsmentioning

confidence: 99%

The impact of Artificial Intelligence on Data System Security: A Literature Review

Rosário¹,

Raimundo²

2021

Preprint

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Diverse forms of artificial intelligence (AI in further text) are at the forefront of triggering digital security innovations, based on the threats that are arising in this post COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control and web domain, just to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods and outage management systems, in an effort to understanding the interplay between AI and those issues. The dependence on the emergence of AI in running industries and shaping the education, transports and health sectors is now well known in literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system secu-rity within the current digital society is opportune. This paper aims at identifying research trends in the field through a Systematic Bibliometric Literature Review (LRSB) of research on AI and system security. The review entails 77 articles published in Scopus® database, presenting up-to-date knowledge on the topic. The LRSB results were synthesized across current research subthemes. Findings are presented. The originality of the paper relies on its LRSB method, together with extant review of articles that have not been categorized so far. Implications for future re-search are suggested.

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