Social engineering: Where does the risk lie? [Editorial]

August 5, 2026 by Moira Prates

Over the years, humanity has become increasingly dependent on technology, and with these advances, it has become increasingly necessary to protect the massive amount of data generated every day. This is where cybersecurity comes in: protecting systems, networks, and data from unauthorized access and various attacks.

For more about this article see link below.

https://ieeexplore.ieee.org/document/11609441

For the open access PDF link of this article please click here.

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

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