Toward a lightweight ontology for privacy protection in IoT

Arruda, Mayke Ferreira; Bulcão-Neto, Renato de Freitas

doi:10.1145/3297280.3297367

Cited by 21 publications

(21 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arruda et al [32] present IoT-Privontology, as a lightweight privacy layer that builds upon IoT concepts expressed in other ontologies. It makes possible to describe policies and requirements related to privacy in IoT, allowing for policy evaluation using ontological approaches.…”

Section: Security Ontologiesmentioning

confidence: 99%

Lightweight Data-Security Ontology for IoT

Gonzalez-Gil

Martı́nez²,

Skarmeta

2020

Sensors

View full text Add to dashboard Cite

Although current estimates depict steady growth in Internet of Things (IoT), many works portray an as yet immature technology in terms of security. Attacks using low performance devices, the application of new technologies and data analysis to infer private data, lack of development in some aspects of security offer a wide field for improvement. The advent of Semantic Technologies for IoT offers a new set of possibilities and challenges, like data markets, aggregators, processors and search engines, which rise the need for security. New regulations, such as GDPR, also call for novel approaches on data-security, covering personal data. In this work, we present DS4IoT, a data-security ontology for IoT, which covers the representation of data-security concepts with the novel approach of doing so from the perspective of data and introducing some new concepts such as regulations, certifications and provenance, to classical concepts such as access control methods and authentication mechanisms. In the process we followed ontological methodologies, as well as semantic web best practices, resulting in an ontology to serve as a common vocabulary for data annotation that not only distinguishes itself from previous works by its bottom-up approach, but covers new, current and interesting concepts of data-security, favouring implicit over explicit knowledge representation. Finally, this work is validated by proof of concept, by mapping the DS4IoT ontology to the NGSI-LD data model, in the frame of the IoTCrawler EU project.

show abstract

Section: Security Ontologiesmentioning

confidence: 99%

Lightweight Data-Security Ontology for IoT

Gonzalez-Gil

Martı́nez²,

Skarmeta

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Also this research article example performance measure discussion is shown in the same Section. "Conclusion" concludes the research work with reference [20,23,28,46]. Kamilaris et al (2019) proposed the Agri-IoT for developing smart farming.…”

Section: Introductionmentioning

confidence: 95%

“…An idea that is suggested above, the separation manual with automated procedure takes being three levels [22] of processing schemes such as lower level, middle level, and higher level. First of all, the lower level is an agreement with the dynamic functioning for maintaining the distinct number of transmission that is operated manually with human hands and a viewing separation by the human being as labor [2,23]. This lower level establishing a concentration with the activeness by hand separation by the human reliability finding.…”

Section: Introductionmentioning

confidence: 97%

“…To maintain the above consideration in the practical verification, this research article to consider and examine "Ontology-based Separation Cognitive Segmentation" by three process schema [23,31]. The "Boosted Continuous Non-spatial whole Attribute Extraction (BCNAE)" for extracting the Sekai-ichi apple attributes such as area (location), compactness (the consistency of a compact solid substances), moments (particular spot identification in a time unit), entropy (a numerical measure of the uncertainty of an outcome).…”

Section: Introductionmentioning

confidence: 99%

“…This helps to make better the recognition result of the ontology framework [29,37]. The "recognition and isolation guarantee" works based on the BCNAE with the original input image from the 3D sensor [20,23,31]. This proposed rhombus based recognition and isolation guarantee spotting method is established and also discrimination between recognition and isolation is distinct from existing spot identification methods, the first recognition and isolation are treated the Origin Input Image(a) samples of the 3D sensor image [30,31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Sanjeevi

Kumar

Prasanna

et al. 2020

Complex Intell. Syst.

View full text Add to dashboard Cite

Constituting the agriculture solid substance manufacture, the post-harvest sector processing schema is direct to preventing reduce the losses in intelligent agriculture. Many processing schemata will be preventing post-harvest losses on the agriculture solid substance manufacture, especially sekai-ichi apple is the regularly used fruit also used to make active in human-related activities of the sensory and control function consisting of an agricultural industry. Sekai-ichi apple is being a definite number of diseases induce, but it is to the highest degree of wastage involving in the Post-Harvest process. Especially sekai-ichi apple count loss is an unsafe many time because it not critically post-harvest. Regardless of consideration, the existing hierarchical model specified post-harvest losses prevention research has deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. This paper suggests a "Hierarchical Model within Ontology Enabled IoT" for distinguishable healthy separation of sekai-ichi apple by using Boosted Continuous Non-spatial whole Attribute Extraction (BCNAE). Sekai-ichi apple count loss is always safe on critically post-harvest. Proposed Post-Harvest hierarchical model specified post-harvest losses prevention and deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. In these suggestions, the separation cognitive operation takes the three levels of processing schemes such as lower level, middle level, and higher level. Firstly, the lower level is express agreements with the dynamic functioning for maintaining the definite number of manual induces. This lower level showing an absorption with the activity of manual separation by the human reliability determination. Secondly, the middle level is an express arrangement with the dynamic functioning for reducing the overfitting and accommodate to fitting the right shape deliberation. Middle level is establishing being generalized by concentrating the time-varying features in the occurrence of a change for the worse identification. Finally, the upper level is express for features refining with the help of the function of sekai-ichi apple image segmentation connection. This interpretability process helps to make the proven position of a prominent classification in a particular fruit on the agriculture solid substance. These three processing flow constructs the ontology structure with manually collected sekai-ichi apple images from a 3D sensor. The observational consequences express that the proposed BCNAE framework recognizes a detection performance carrying out with an optimized-separation ratio for time-variant of the separation process.

show abstract

Security Ontologies: An Investigation of Pitfall Rate

Patel¹,

Debnath²

2022

Data Science With Semantic Technologies

View full text Add to dashboard Cite

Toward a lightweight ontology for privacy protection in IoT

Cited by 21 publications

References 14 publications

Lightweight Data-Security Ontology for IoT

Lightweight Data-Security Ontology for IoT

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Security Ontologies: An Investigation of Pitfall Rate

Contact Info

Product

Resources

About