Managing virtualized services efficiently over the cloud is an open challenge. Traditional models of software development are not appropriate for the cloud computing domain, where software (and other) services are acquired on demand. In this paper, we describe a new integrated methodology for the lifecycle of IT services delivered on the cloud, and demonstrate how it can be used to represent and reason about services and service requirements and so automate service acquisition and consumption from the cloud. We have divided the IT service lifecycle into five phases of requirements, discovery, negotiation, composition, and consumption. We detail each phase and describe the ontologies that we have developed to represent the concepts and relationships for each phase. To show how this lifecycle can automate the usage of cloud services, we describe a cloud storage prototype that we have developed. This methodology complements previous work on ontologies for service descriptions in that it is focused on supporting negotiation for the particulars of a service and going beyond simple matchmaking.
Medical organizations find it challenging to adopt cloud-based electronic medical records services, due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient centric approach for EHR management where the responsibility of authorizing data access is handled at the patients' end. This however creates a significant overhead for the patient who has to authorize every access of their health record. This is not practical given the multiple personnel involved in providing care and that at times the patient may not be in a state to provide this authorization. Hence there is a need of developing a proper authorization delegation mechanism for safe, secure and easy cloud-based EHR management. We have developed a novel, centralized, attribute based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHR's access authority to the medical providers. In this paper, we describe this novel ABE approach as well as the prototype system that we have created to illustrate it.
Big data analytics related to consumer behavior, market analysis, opinions, and recommendation often deal with end user's derived and inferred data, along with the observed data. To ensure consumer data protection, rules defined by the European Union's General Data Protection Regulation (EU GDPR) must be adhered to by every organization using Personally Identifiable Information (PII) data for Big Data analysis. Similarly, Payment Card Industry Data Security Standard (PCI DSS) has policy guidelines specifically for organizations handling consumer's payment card data. Both data regulation policies are currently available only in textual format and require significant manual effort to ensure their compliance. We have developed an integrated, semantically rich Knowledge Graph (or Ontology) to represent the rules mandated by both PCI DSS and EU GDPR. In the Ontology, we have also identified the obligations defined in these regulations and related them with corresponding Cloud Security Alliance (CSA) controls. We have validated this Knowledge Graph against the data policies of major vendors that deal with Big Data. This Knowledge Graph that is available in the public domain can be used by Big Data practitioners to automate data protection compliance in their organization.
Cloud services are becoming an essential part of many organizations. Cloud providers have to adhere to security and privacy policies to ensure their users' data remains confidential and secure. Though there are some ongoing efforts on developing cloud security standards, most cloud providers are implementing a mishmash of security and privacy controls. This has led to confusion among cloud consumers as to what security measures they should expect from the cloud services, and whether these measures would comply with their security and compliance requirements. We have conducted a comprehensive study to review the potential threats faced by cloud consumers and have determined the compliance models and security controls that should be in place to manage the risk. Based on this study, we have developed an ontology describing the cloud security controls, threats and compliances. We have also developed an application that classifies the security threats faced by cloud users and automatically determines the high level security and compliance policy controls that have to be activated for each threat. The application also displays existing cloud providers that support these security policies. Cloud consumers can use our system to formulate their security policies and find compliant providers even if they are not familiar with the underlying technology.
Digital Twin models are computerized clones of physical assets that can be used for in-depth analysis. Industrial production lines tend to have multiple sensors to generate near real-time status information for production. Industrial Internet of ings datasets are di cult to analyze and infer valuable insights such as points of failure, estimated overhead. etc. In this paper we introduce a simple way of formalizing knowledge as digital twin models coming from sensors in industrial production lines. We present a way on to extract and infer knowledge from large scale production line data, and enhance manufacturing process management with reasoning capabilities, by introducing a semantic query mechanism. Our system primarily utilizes a graph-based query language equivalent to conjunctive queries and has been enriched with inference rules.
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