Browsing by Author "Katarahweire, Marriette"

Now showing 1 - 4 of 4
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    Data Classification for Secure Mobile Health Data Collection Systems
    (Development Engineering, 2020) Katarahweire, Marriette; Bainomugisha, Engineer; Mughal, Khalid A.
    Data collected in Mobile Health Data Collections Systems (MHDCS) are diverse, both in terms of type and value. This calls for different data protection measures to meet security goals of confidentiality, integrity, and availability. The majority of commonly used open-source MHDCS track and monitor individuals over a while. It is therefore important to have sensitive data defined and proper security measures identified. We propose a data classification model as a basis for secure design and implementation. Our method combines interviews with case studies. The case studies focused on three of the widely used MHDCS platforms in low-resource settings; that is Muzima, Open Data Kit (ODK), and District Health Information Software (DHIS) 2 Tracker Capture. Interviews with domain experts helped define the sensitivity of data in MHDCS. The proposed data classification model provides for three sensitivity levels: public, confidential, and critical. The model uses context information and multiple parameters as inputs to a classification scheme that maps data to sensitivity levels. The generated data classifications are intended to guide developers and users to build security into MHDCS starting from the early stages of the software development life cycle.
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    Multi-Factor Authentication for Enhanced Security of Mobile Health Data Collection Systems
    (Makerere University, 2020) Kalega, Ausse; Bainomugisha, Engineer; Tumwesigye, Nazarius M.; Katarahweire, Marriette; Mughal, Khalid Azim
    This paper describes an additional enhanced authentication method for Mobile Health Data Collection Systems (MHDCS) compared to the commonly existing method of using usernames and passwords for field data collectors. The mechanism introduced in this study was developed to overcome the authentication security challenges faced during the collection of electronic primary health data through MHDCS by adding location and time as additional features of user authentication.
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    Scenario-based Synthetic Dataset Generation for Mobile Money Transactions
    (Federated Africa and Middle East Conference on Software Engineering, 2022) Azamuke, Denish; Katarahweire, Marriette; Bainomugisha, Engineer
    There is limited availability of mobile money transaction datasets from Sub-Saharan Africa for research because transaction data records are sensitive in nature and therefore raise privacy concerns. This has in turn hindered the potential to study fraudulent patterns in mobile money transactions so as to propose realistic mitigation measures based on Machine Learning Approaches to the prevailing financial fraud challenges in the region. This research presents mobile money scenarios that should be considered in order to implement a simulator that can harness synthetic datasets for mobile money transactions from Sub-Saharan Africa so as to carry out fraud detection research. These scenarios include the definition of a mobile money ecosystem with processes used by actors such as mobile money agents, clients, merchants and banks to interact with each other in mobile money operations. There is also a need for a real mobile money dataset to extract statistical information and diverse fraudulent behaviours of actors and fraud examples in mobile money markets. This research uses the design considerations to examine process-driven techniques such as numerical simulation, agent-based modeling, and data-driven techniques such as neural networks that can be leveraged to generate synthetic datasets for mobile money transactions. Common data generation toolkits like PaySim, AMLSim, RetSim and ABIDES that are based on these techniques have been examined. The design considerations are used to design a realistic model known as MoMTSim based on real mobile money processes and agent-based modeling techniques that can be implemented to generate synthetic transaction datasets for mobile money with fraud instances. This will facilitate fraud detection research. The synthetic datasets eliminate data privacy risks, are easy and faster to obtain, and are cheap to experiment with. With the proposed model, different research groups can move to the implementation stage to realise a model for synthetic data generation for mobile money transactions from the Sub-Saharan region.
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    Scenario-based Synthetic Dataset Generation for Mobile Money Transactions
    (In Proceedings of the Federated Africa and Middle East Conference on Software Engineering, 2022) Azamuke, Denish; Katarahweire, Marriette; Bainomugisha, Engineer
    There is limited availability of mobile money transaction datasets from Sub-Saharan Africa for research because transaction data records are sensitive in nature and therefore raise privacy concerns. This has in turn hindered the potential to study fraudulent patterns in mobile money transactions so as to propose realistic mitigation measures based on Machine Learning Approaches to the prevailing financial fraud challenges in the region. This research presents mobile money scenarios that should be considered in order to implement a simulator that can harness synthetic datasets for mobile money transactions from Sub-Saharan Africa so as to carry out fraud detection research. These scenarios include the definition of a mobile money ecosystem with processes used by actors such as mobile money agents, clients, merchants and banks to interact with each other in mobile money operations. There is also a need for a real mobile money dataset to extract statistical information and diverse fraudulent behaviours of actors and fraud examples in mobile money markets. This research uses the design considerations to examine process-driven techniques such as numerical simulation, agent-based modeling, and data-driven techniques such as neural networks that can be leveraged to generate synthetic datasets for mobile money transactions. Common data generation toolkits like PaySim, AMLSim, RetSim and ABIDES that are based on these techniques have been examined. The design considerations are used to design a realistic model known as MoMTSim based on real mobile money processes and agent-based modeling techniques that can be implemented to generate synthetic transaction datasets for mobile money with fraud instances. This will facilitate fraud detection research. The synthetic datasets eliminate data privacy risks, are easy and faster to obtain, and are cheap to experiment with. With the proposed model, different research groups can move to the implementation stage to realise a model for synthetic data generation for mobile money transactions from the Sub-Saharan region.