Browsing by Author "Mireku Kwakye, Michael"

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    ItemOpen Access
    Light-weight Privacy Infrastructure - A Blockchain-based Privacy-Preservation Platform for Data Storage and Query Processing
    (2022-06) Mireku Kwakye, Michael; Barker, Kenneth Edwin; Jacobson Jr., Michael John; Reardon, Joel Christopher; Ray, Suprio; Far, Behrouz Homayoun
    Privacy-preservation policies are guidelines and recommendations formulated to protect data provider’s private, sensitive data in data repositories. These policies are implemented using privacy-preservation methodologies. Previous privacy-preservation methodologies have addressed privacy in which data are permanently stored in repositories and disconnected from changing data provider privacy preferences. This becomes evident as the data moves to another data repository. The ability of data providers to flexibly update or change their privacy preferences when it is required is a known challenge. Moreover, the ability for data providers to control their existing privacy preferences due to changes in data usage continues to remain a problem. This research proposes a Light-weight Privacy Infrastructure (LPI); which is a methodology/framework for privacy-preservation of data provider’s private and sensitive data. The approach offers data providers flexibility to easily change and monitor privacy preferences on their stored data when the data usage requirements change. Additionally, the approach offers data providers control over access and usage of their private, sensitive data by data collectors and/or accessors and third-party data accessors. The research proposes to tightly couple data provider’s private attribute data element to privacy preferences and data accessor data elements. The implementation presents a framework of tightly-coupled relational Database Management System (DBMS), blockchains, and genomic data store. The coupled database framework delivers a secure and query-efficient platform for management and query processing of data provider’s private data. The implementation adopts an Alberta biotechnology platform that provides commercial oncogenomic services, as a case study. The healthcare platform processes both cancer-related healthcare data and next generation sequencing (NGS) genomic data. Data privacy in healthcare data is a necessary requirement in the processing of data provider private and sensitive data across varied data repositories. The implementation provides data providers (i.e., patients) and data collectors and/or accessors (for e.g., physicians) the platform to efficiently manage data whiles eliminating the risks of privacy breaches and unauthorized data access. The major contributions are: first, provide an approach to tightly couple data provider private, sensitive data with privacy preferences, and data accessor data elements into a privacy tuple. Second, provide a tightly-coupled immutable, tamper-resistant data processing platform where data providers monitor and control all forms of access to their private, sensitive data. Third, provide implementation of a privacy infrastructure where data providers have maximum flexibility to change their privacy preferences on all transactions processed on their underlying private, sensitive data without requiring the data collector. Finally, provide an implementation framework applicable to healthcare and genomic data processing that uses a biotechnology platform as a case study. The evaluation analysis from the implementation procedures offers a validation for the research based on the query processing output of privacy-aware queries on the privacy infrastructure.
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    ItemOpen Access
    Modelling and Design of Generic Semantic Trajectory Data Warehouse
    (2017-01-26) Mireku Kwakye, Michael
    The trajectory patterns of a moving object in a spatio-temporal domain offers varied information in terms of the management of the data generated from the movement. A trajectory data warehouse is a data repository for the data and information of trajectory objects and their associated spatial objects for defined temporal periods. The query results of trajectory objects from the data warehouse are usually not enough to answer certain trend behaviours and meaningful inferences without the associated semantic information of the trajectory object or the geospatial environment within a specified purpose or context. In this report, I formulate and design a generic ontology modelling framework that serves as the background model platform for the design of a semantic data warehouse for trajectories. This semantic trajectory data warehouse can be adaptable for trajectory data processing and analytics on any chosen spatio-temporal application domain. The methodology underpins on higher granularity of data as a result of pre-processed and transformed ETL data so as to offer efficient semantic inference to the underlying trajectory data. Moreover, the approach outlines the thematic dimensions that serve as necessary entities for extracting semantic information. Additionally, the modelling approach offers a design platform for effective predictive trend analysis and knowledge discovery in the trajectory dynamics and data processing for moving objects.