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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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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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Ali Alshehri, Mohamed Baza, Gautam Srivastava, Wahid Rajeh, Majed Alrowaily and Majed Almusali
With the advancement of cyber threats, blockchain technology has evolved to have a significant role in providing secure and reliable decentralized applications. One of these applications is a remote voting system that allow voters to participate in elect...
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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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Mohit Kumar, Bernhard A. Moser, Lukas Fischer and Bernhard Freudenthaler
In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving interpret...
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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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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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Yiming Zhu, Dehua Zhou, Yuan Li, Beibei Song and Chuansheng Wang
Recently, significant progress has been made in the field of public key encryption with keyword search (PEKS), with a focus on optimizing search methods and improving the security and efficiency of schemes. Keyword frequency analysis is a powerful tool f...
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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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Nigang Sun, Chenyang Zhu, Yuanyi Zhang and Yining Liu
Digital transformation of the logistics industry triggered by the widespread use of Internet of Things (IoT) technology has prompted a significant revolution in logistics companies, further bringing huge dividends to society. However, the concurrent acce...
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