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  • 1.
    Alkhabbas, Fahed
    et al.
    Malmö University, Malmo, Sweden; Malmö University, Malmo, Sweden.
    Alsadi, Mohammed
    Norwegian University Of Science And Technology, Trondheim, Norway.
    Alawadi, Sadi
    Halmstad University, School of Information Technology. Uppsala University, Uppsala, Sweden.
    Awaysheh, Feras M.
    University Of Tartu, Tartu, Estonia.
    Kebande, Victor R.
    Blekinge Institute Of Technology, Karlskrona, Sweden.
    Moghaddam, Mahyar T.
    University Of Southern Denmark, Odense, Denmark.
    ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 18, article id 6842Article in journal (Refereed)
    Abstract [en]

    Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems’ environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems’ security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach’s feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems’ constituents to learn about security threats in their environments collaboratively. © 2022 by the authors.

  • 2.
    Galozy, Alexander
    et al.
    Halmstad University, School of Information Technology.
    Alawadi, Sadi
    Halmstad University, School of Information Technology.
    Kebande, Victor
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology.
    Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit StudyManuscript (preprint) (Other academic)
    Abstract [en]

    This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task. Our study contributes to the growing body of research on privacy-preserving machine learning and underscores the need for tailored privacy techniques that address specific attack patterns rather than relying on one-size-fits-all solutions. We use the latent bandit setting to evaluate the trade-off between privacy and recommender performance by employing various aggregation strategies, such as averaging, nearest neighbor, and clustering combined with noise injection. More specifically, we simulate a linkage attack scenario leveraging publicly available auxiliary information acquired by the adversary. Our results on three open real-world datasets reveal that adding noise using the Laplace mechanism to an individual user's data record is a poor choice. It provides the highest regret for any noise level, relative to de-anonymization probability and the ADS metric. Instead, one should combine noise with appropriate aggregation strategies. For example, using averages from clusters of different sizes provides flexibility not achievable by varying the amount of noise alone. Generally, no single aggregation strategy can consistently achieve the optimum regret for a given desired level of privacy.

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