The Experts below are selected from a list of 2487 Experts worldwide ranked by ideXlab platform

Mukesh Mohania - One of the best experts on this subject based on the ideXlab platform.

  • Security, Compliance, and Agile Deployment of personal identifiable information Solutions on a Public Cloud
    2016 IEEE 9th International Conference on Cloud Computing (CLOUD), 2016
    Co-Authors: Yasuharu Katsuno, Ashish Kundu, Robert Schloss, Hitomi Takahashi, Mukesh Mohania
    Abstract:

    A public cloud platform offers economy of scale, ease of management, and elasticity to solutions. In addition, regulatory compliance and security must be assured for solutions handling sensitive data, such as student and healthcare data. With the steep rise in data breaches at large enterprises, it is a requirement to emphasize the security, privacy, and compliance of cloud-delivered solutions that hold personally identifiable information (PII). An example of a solution in need of such assurances is an education and learning-related analytics service that handles confidential student data on a public cloud platform. In this paper, we propose an approach for managing the security and privacy of an education and learning-analytics solution on a public cloud platform while assuring compliance with the Family Educational Rights and Privacy Act (FERPA). We also propose a new agile deployment approach that is both rapid and automatic. A prototype of a learning-analytics solution was implemented on a SoftLayer public cloud, and the new deployment method was evaluated in comparison with existing methods.

  • CLOUD - Security, Compliance, and Agile Deployment of personal identifiable information Solutions on a Public Cloud
    2016 IEEE 9th International Conference on Cloud Computing (CLOUD), 2016
    Co-Authors: Yasuharu Katsuno, Ashish Kundu, Hitomi Takahashi, Koushik K. Das, Robert Jeffrey Schloss, Prasenjit Dey, Mukesh Mohania
    Abstract:

    A public cloud platform offers economy of scale, ease of management, and elasticity to solutions. In addition, regulatory compliance and security must be assured for solutions handling sensitive data, such as student and healthcare data. With the steep rise in data breaches at large enterprises, it is a requirement to emphasize the security, privacy, and compliance of cloud-delivered solutions that hold personally identifiable information (PII). An example of a solution in need of such assurances is an education and learning-related analytics service that handles confidential student data on a public cloud platform. In this paper, we propose an approach for managing the security and privacy of an education and learning-analytics solution on a public cloud platform while assuring compliance with the Family Educational Rights and Privacy Act (FERPA). We also propose a new agile deployment approach that is both rapid and automatic. A prototype of a learning-analytics solution was implemented on a SoftLayer public cloud, and the new deployment method was evaluated in comparison with existing methods.

Sabah Al-fedaghi - One of the best experts on this subject based on the ideXlab platform.

  • Privacy Things: Systematic Approach to Privacy and personal identifiable information
    arXiv: Computers and Society, 2018
    Co-Authors: Sabah Al-fedaghi
    Abstract:

    Defining privacy and related notions such as personal identifiable information (PII) is a central notion in computer science and other fields. The theoretical, technological, and application aspects of PII require a framework that provides an overview and systematic structure for the discipline’s topics. This paper develops a foundation for representing information privacy. It introduces a coherent conceptualization of the privacy senses built upon diagrammatic representation. A new framework is presented based on a flow-based model that includes generic operations performed on PII.

  • Experimentation with personal identifiable information
    Intelligent Information Management, 2012
    Co-Authors: Sabah Al-fedaghi, Abdul Aziz Rashid Al-azmi
    Abstract:

    In this paper, actual personal identifiable information (PII) texts are analyzed to capture different types of PII sensitivities. The sensitivity of PII is one of the most important factors in determining an individual’s perception of privacy. A “gradation” of sensitivity of PII can be used in many applications, such as deciding the security level that controls access to data and developing a measure of trust when self-disclosing PII. This paper experiments with a theoretical analysis of PII sensitivity, defines its scope, and puts forward possible methodologies of gradation. A technique is proposed that can be used to develop a classification scheme of personal information depending on types of PII. Some PII expresses relationships among persons, some specifies aspects and features of a person, and some describes relationships with nonhuman objects. Results suggest that decomposing PII into privacy-based portions helps in factoring out non-PII information and focusing on a proprietor’s related information. The results also produce a visual map of the privacy sphere that can be used in approximating the sensitivity of different territories of privacy-related text. Such a map uncovers aspects of the proprietor, the proprietor’s relationship to social and physical entities, and the relationships he or she has with others.

  • Engineering Privacy Revisited
    Journal of Computer Science, 2012
    Co-Authors: Sabah Al-fedaghi
    Abstract:

    Problem statement: information Privacy Engineering (IPE) is the field that studies the protection of privacy in information and communication systems. The theoretical, technological and applications aspects of IPE require a framework that provides a general view and a systematic structure for the discipline’s topics. This study discusses certain characteristics of such a framework and proposes enhancing and strengthening its structure. Approach: Several important problems and their solutions are presented through recasting of some current proposed approaches to personal identifiable information (PII) definitions and handling. Results: A new framework is presented that is based on flow-based model, along with generic operations performed on PII. Conclusion: This study shows that the flow-based model can provide a structure that complements current efforts to develop a framework for IPE.

  • A Flow-Based Model to Assess Privacy Impact
    2011
    Co-Authors: Sabah Al-fedaghi, Abdilhadi Jeragh
    Abstract:

    Privacy regulations and laws have been introduced to control handling of information about persons. Compliance with these regulations and laws presents a significant challenge for organizations holding data about identifiable persons. A privacy impact assessment (PIA) is an important instrument for ensuring conformity to regulatory requirements, determining risks, and evaluating privacy protections. This paper scrutinizes recent approaches to PIA and introduces a foundation for such a process. This foundation is used to reformulate basic notions such as definition, handling, and uses of personal identifiable information. As an example case, we apply our methodology to the 2010 US Department of Homeland Security’s guidance on PIA.

  • AAAI Spring Symposium: Intelligent information Privacy Management - information Privacy and its Value.
    2010
    Co-Authors: Sabah Al-fedaghi
    Abstract:

    This paper discusses ethics-based conceptualization of information privacy and its moral value. We analyze Floridi’s reductionist, ownership-based, and ontological interpretations of information privacy according to personal identifiable information. It is shown that ontological interpretation has a social dimension related to personal identifiable information that is proprietarily shared among several persons. We also anatomize the relationship between physical and informational spheres in terms of a flow model of activities of patients and personal identifiable information and actions of agents.

Benjamin Yankson - One of the best experts on this subject based on the ideXlab platform.

  • Continuous improvement process (CIP)-based privacy-preserving framework for smart connected toys
    International Journal of Information Security, 2021
    Co-Authors: Benjamin Yankson
    Abstract:

    Advances within the toy industry and interconnectedness have resulted in the rapid and pervasive development of smart connected toys (SCTs), built with the capacity to collect terabytes of personal identifiable information, device context data, and play data. Any compromise of data stored, process, or transit can introduce privacy concerns, financial fraud concerns, and safety concerns, such as location identification, which can put child physical safety at risk. This work provides an overview of the SCT privacy problem and the landscape based on previous work to address privacy and safety concerns. We further investigate technical and legislative-related privacy issues in SCT and present a confirmatory study on developers’ privacy views based on the privacy calculus theory. Finally, we present an abstract continuous improvement process-based privacy preservation framework using Plan-Do-Check-Act, which can be adopted and used during the SCT design to minimize privacy breaches and serve as a foundation for the deployment of continuous privacy control improvement in a complex SCT development context.

S Meschgustavo - One of the best experts on this subject based on the ideXlab platform.

Yasuharu Katsuno - One of the best experts on this subject based on the ideXlab platform.

  • Security, Compliance, and Agile Deployment of personal identifiable information Solutions on a Public Cloud
    2016 IEEE 9th International Conference on Cloud Computing (CLOUD), 2016
    Co-Authors: Yasuharu Katsuno, Ashish Kundu, Robert Schloss, Hitomi Takahashi, Mukesh Mohania
    Abstract:

    A public cloud platform offers economy of scale, ease of management, and elasticity to solutions. In addition, regulatory compliance and security must be assured for solutions handling sensitive data, such as student and healthcare data. With the steep rise in data breaches at large enterprises, it is a requirement to emphasize the security, privacy, and compliance of cloud-delivered solutions that hold personally identifiable information (PII). An example of a solution in need of such assurances is an education and learning-related analytics service that handles confidential student data on a public cloud platform. In this paper, we propose an approach for managing the security and privacy of an education and learning-analytics solution on a public cloud platform while assuring compliance with the Family Educational Rights and Privacy Act (FERPA). We also propose a new agile deployment approach that is both rapid and automatic. A prototype of a learning-analytics solution was implemented on a SoftLayer public cloud, and the new deployment method was evaluated in comparison with existing methods.

  • CLOUD - Security, Compliance, and Agile Deployment of personal identifiable information Solutions on a Public Cloud
    2016 IEEE 9th International Conference on Cloud Computing (CLOUD), 2016
    Co-Authors: Yasuharu Katsuno, Ashish Kundu, Hitomi Takahashi, Koushik K. Das, Robert Jeffrey Schloss, Prasenjit Dey, Mukesh Mohania
    Abstract:

    A public cloud platform offers economy of scale, ease of management, and elasticity to solutions. In addition, regulatory compliance and security must be assured for solutions handling sensitive data, such as student and healthcare data. With the steep rise in data breaches at large enterprises, it is a requirement to emphasize the security, privacy, and compliance of cloud-delivered solutions that hold personally identifiable information (PII). An example of a solution in need of such assurances is an education and learning-related analytics service that handles confidential student data on a public cloud platform. In this paper, we propose an approach for managing the security and privacy of an education and learning-analytics solution on a public cloud platform while assuring compliance with the Family Educational Rights and Privacy Act (FERPA). We also propose a new agile deployment approach that is both rapid and automatic. A prototype of a learning-analytics solution was implemented on a SoftLayer public cloud, and the new deployment method was evaluated in comparison with existing methods.