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Alya Alshammari and Khalil El Hindi
The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource r...
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Hanyue Xu, Kah Phooi Seng, Jeremy Smith and Li Minn Ang
In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the co...
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Ishaani Priyadarshini
The swift proliferation of the Internet of Things (IoT) devices in smart city infrastructures has created an urgent demand for robust cybersecurity measures. These devices are susceptible to various cyberattacks that can jeopardize the security and funct...
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Ali Eghmazi, Mohammadhossein Ataei, René Jr Landry and Guy Chevrette
The Internet of Things (IoT) is a technology that can connect billions of devices or ?things? to other devices (machine to machine) or even to people via an existing infrastructure. IoT applications in real-world scenarios include smart cities, smart hou...
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Sebastián Vallejos, Luis Berdun, Marcelo Armentano, Silvia Schiaffino and Daniela Godoy
Data captured by mobile devices enable us, among other things, learn the places where users go, identify their home and workplace, the places they usually visit (e.g., supermarket, gym, etc.), the different paths they take to move from one place to anoth...
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Zacharias Anastasakis, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Stavroula Bourou, Konstantinos Psychogyios, Dimitrios Skias and Theodore Zahariadis
Federated Learning is identified as a reliable technique for distributed training of ML models. Specifically, a set of dispersed nodes may collaborate through a federation in producing a jointly trained ML model without disclosing their data to each othe...
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Gurtaj Singh, Vincenzo Violi and Marco Fisichella
Healthcare data are distributed and confidential, making it difficult to use centralized automatic diagnostic techniques. For example, different hospitals hold the electronic health records (EHRs) of different patient populations; however, transferring t...
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Duy Tung Khanh Nguyen, Dung Hoang Duong, Willy Susilo, Yang-Wai Chow and The Anh Ta
Homomorphic encryption (HE) has emerged as a pivotal technology for secure neural network inference (SNNI), offering privacy-preserving computations on encrypted data. Despite active developments in this field, HE-based SNNI frameworks are impeded by thr...
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Farid Lalem, Abdelkader Laouid, Mostefa Kara, Mohammed Al-Khalidi and Amna Eleyan
Digital signature schemes are practical mechanisms for achieving message integrity, authenticity, and non-repudiation. Several asymmetric encryption techniques have been proposed in the literature, each with its proper limitations. RSA and El Gamal prove...
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Yongli Tang, Deng Pan, Panke Qin and Liping Lv
Federal learning and privacy protection are inseparable. The participants in federated learning need to be the targets of privacy protection. On the other hand, federated learning can also be used as a tool for privacy attacks. Group signature is regarde...
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