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Context-Based Multi-Tenancy Policy Enforcement For Data Sharing In IoT Systems

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2021, Master of Computer Science (M.C.S.), University of Dayton, Computer Science.
Nowadays, real-time data originating from connected devices are crucial to the Internet of Things (IoT) real-world applications. IoT data are also shared with multiple parties and tenants for their business based on contract agreements. For instance, in smart cities, most stores and restaurants have indoor surveillance cameras for security purposes. Stores also use these camera systems to enhance their customer experience by analyzing customer’s behaviors, usually performed by AI applications from third parties. At the same time, the cameras might be shared with the local police department in case of emergency events. Such sharing scenarios face many challenges, including heterogeneous IoT protocols and platforms, real-time or near real-time processing, and the privacy and security of the data. The existing IoT data marketplaces provide mechanisms to share data; however, they did not address the issues where sharing contracts are dynamically changed depending on the context. This thesis proposes a novel IoT Datahub architecture with a dynamic context-based policy enforcement framework that supports IoT data sharing based on dynamic contracts. Our enforcement framework allows IoT data providers to define extensible rules and metrics to govern the tenants in accessing the shared data based on policies defined in static and dynamic contexts. For example, for the same real-time data stream, we can define and enforce a policy that allows some authorized tenants to access while only allowing some other tenants to access the data in some circumstances, such as emergencies. We have developed a proof-of-concept prototype with a surveillance camera system to illustrate our proposed framework. Our system demonstrates that we can define and enforce dynamic policies for accessing camera data based on the context from the camera, e.g., the number of people currently captured in the camera after a given period for selected authorized tenants
Phu Phung (Advisor)
James Buckley (Committee Member)
Zhongmei Yao (Committee Member)
66 p.

Recommended Citations

Citations

  • Nguyen, H. H. (2021). Context-Based Multi-Tenancy Policy Enforcement For Data Sharing In IoT Systems [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628158560712128

    APA Style (7th edition)

  • Nguyen, Huu Ha. Context-Based Multi-Tenancy Policy Enforcement For Data Sharing In IoT Systems. 2021. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628158560712128.

    MLA Style (8th edition)

  • Nguyen, Huu Ha. "Context-Based Multi-Tenancy Policy Enforcement For Data Sharing In IoT Systems." Master's thesis, University of Dayton, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628158560712128

    Chicago Manual of Style (17th edition)