A Semantic Approach for Automating Knowledge in Policies of Cyber Insurance Services

Joshi, Ketki; Joshi, Karuna Pande; Mittal, Sudip

doi:10.1109/icws.2019.00018

Cited by 7 publications

(12 citation statements)

References 7 publications

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“…These will help create robust cyber insurance markets for precision agriculture. Various systems [133]- [136] also need to be built to make it easier for the end users to understand and parse these complex legal documents, cyber insurance policies, agreements, contracts, etc. Research on these open challenges will help wide adoption of precision agriculture technologies.…”

Section: Compliance and Supply Chain Perspectivementioning

confidence: 99%

Security and Privacy in Smart Farming: Challenges and Opportunities

et al. 2020

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Internet of Things (IoT) and smart computing technologies have revolutionized every sphere of 21 st century humans. IoT technologies and the data driven services they offer were beyond imagination just a decade ago. Now, they surround us and influence a variety of domains such as automobile, smart home, healthcare, etc. In particular, the Agriculture and Farming industries have also embraced this technological intervention. Smart devices are widely used by a range of people from farmers to entrepreneurs. These technologies are used in a variety of ways, from finding real-time status of crops and soil moisture content to deploying drones to assist with tasks such as applying pesticide spray. However, the use of IoT and smart communication technologies introduce a vast exposure to cybersecurity threats and vulnerabilities in smart farming environments. Such cyber attacks have the potential to disrupt the economies of countries that are widely dependent on agriculture. In this paper, we present a holistic study on security and privacy in a smart farming ecosystem. The paper outlines a multi layered architecture relevant to the precision agriculture domain and discusses the security and privacy issues in this dynamic and distributed cyber physical environment. Further more, the paper elaborates on potential cyber attack scenarios and highlights open research challenges and future directions. INDEX TERMS Security, privacy, smart farming, precision agriculture, cloud computing, edge computing, cyber physical systems, IoT, artificial intelligence (AI), machine learning, layered architecture.

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Section: Compliance and Supply Chain Perspectivementioning

confidence: 99%

Security and Privacy in Smart Farming: Challenges and Opportunities

et al. 2020

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“…Legal Ontology [71]- [73] helps in semantic indexing and search, keeping track of recent changes in laws, etc. The cyber insurance ontology [74] represents various insurance policies which would provide the insurance seekers with necessary details such as policy coverage, coverage limits, expected rate, etc.…”

Section: Ontology Based System For Cpsmentioning

confidence: 99%

“…The cooperatives laid emphasis on economic efficiency and environment effectiveness to avoid water conflicts in the agriculture sector. Many researchers have previously worked on creating various AI systems for legal text [72]- [74], [77], [78]. These enable consumers to efficiently manage, monitor, and validate legal contracts, such as Service Level Agreements (SLAs) and privacy policies, that are available as text documents.…”

Section: Cooperative Agreementsmentioning

confidence: 99%

Ontologies and Artificial Intelligence Systems for the Cooperative Smart Farming Ecosystem

et al. 2020

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Cyber-Physical Systems (CPS) and Internet of Thing (IoT) generate large amounts of data spurring the rise of Artificial Intelligence (AI) based smart applications. Driven by rapid advancements in technologies that support smart devices, agriculture and farming sector is shifting towards IoT connected ecosystem to balance the increase in demand for food supply. As the number of smart farms reach critical mass, it is now possible to include AI assisted systems at a cooperative (co-op) farming level. Today, in the United States alone there are about 1,871 coops serving 1,890,057 member farmers. Hence, such advanced technologies and infrastructure when incorporated in the coop farming ecosystem can immensely benefit small member farmers who operate and maintain these independent coop entities. In this paper, we develop a connected cooperative ecosystem which defines sensors and their communication among different entities along with cloud supported coop hub. We develop member farm and coop ontologies to capture data and various interactions that happen between shared resources, member farms, and the coop that are stored in the cloud. These can then help generate AI supported insights for farmers and the cooperative. Several coop farming use case scenarios have been discussed to demonstrate the functioning of our smart cooperative ecosystem. Finally, the paper describes various AI applications that can be deployed at the coop level to aid member farmers.

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“…The resulting potential for inappropriate dissemination and usage of a given consumer's private information has raised concern among the public [3], prompting the creation of a plethora of data protection regulations like the Payment Card Industry Data Security Standard (PCI DSS) [11], the European Union's General Data Protection Regulation (GDPR) [1], and the Children's Online Privacy Protection Act (COPPA) [10]. A key element of this process is the extraction of these policies from text and representing them as Knowledge Graphs [18], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Text Ambiguity on creating Policy Knowledge Graphs

Kotal

Joshi

2021

2021 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Big Data &Amp; Cloud Computing, Sustainable Com

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A growing number of web and cloud-based products and services rely on data sharing between consumers, service providers, and their subsidiaries and third parties. There is a growing concern around the security and privacy of data in such large-scale shared architectures. Most organizations have a human-written privacy policy that discloses all the ways that data is shared, stored, and used. The organizational privacy policies must also be compliant with government and administrative regulations. This raises a major challenge for providers as they try to launch new services. Thus they are moving towards a system of automatic policy maintenance and regulatory compliance. This requires extracting policy from text documents and representing it in a semi-structured, machine-processable framework. The most popular method to this end is extracting policy information into a Knowledge Graph (KG). There exists a significant body of work that converts text descriptions of regulations into policies expressed in languages such as OWL and XACML and is grounded in the control-based schema by using NLP approaches. In this paper, we show that the NLP-based approaches to extract knowledge from written policy documents and representing them in enforceable Knowledge Graphs fail when the text policies are ambiguous. Ambiguity can arise from lack of clarity, misuse of syntax, and/or the use of complex language. We describe a system to extract features from a policy document that affect its ambiguity and classify the documents based on the level of ambiguity present. We validate this approach using human annotators. We show that a large number of documents in a popular privacy policy corpus (OPP-115) are ambiguous. This affects the ability to automatically monitor privacy policies. We show that for policies that are more ambiguous according to our proposed measure, NLP-based text segment classifiers are less accurate.

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A Semantic Approach for Automating Knowledge in Policies of Cyber Insurance Services

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Cited by 7 publications

References 7 publications

Security and Privacy in Smart Farming: Challenges and Opportunities

Security and Privacy in Smart Farming: Challenges and Opportunities

Ontologies and Artificial Intelligence Systems for the Cooperative Smart Farming Ecosystem

The Effect of Text Ambiguity on creating Policy Knowledge Graphs

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