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Smart contracts

A primer on blockchain and queueing theory: Understanding transaction flows through waiting lines

August 5, 2026 by Shamal Lalvani, Aggelos Katsaggelos

Blockchains are decentralized systems that allow for transactions among parties to be securely processed without the need for a central intermediary, such as a bank in the case of currency exchange. Since the inception of Bitcoin (the first implementation of blockchain) in 2009, considerable advancements have been made in the field of blockchain, spanning applications to … [Read more...] about A primer on blockchain and queueing theory: Understanding transaction flows through waiting lines

A beginner’s guide to plagiarism detection using blockchain

September 15, 2024 by Saroja S

Plagiarism is a big issue in research. Academic dishonesty is a long-standing issue for higher education service providers. However, the increased availability of information sources that student writers may easily access, as well as essay-writing services aggressively advertised to university students, has resulted in a rise in discussions on this problem in both the media and … [Read more...] about A beginner’s guide to plagiarism detection using blockchain

SolSec Labs: Virtual labs for learning smart contract security

February 10, 2023 by Devang Jain

SolSec Labs is a web-based application that provides interactive training labs for blockchain developers, especially students eager to learn more about smart contract security to gain practical knowledge ( Fig. 1 ). It teaches solidity and smart contract security skills through hands-on experience. The easy-to-understand environment enables students and early developers to … [Read more...] about SolSec Labs: Virtual labs for learning smart contract security

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About the Magazine

IEEE Potentials Magazine is the publication dedicated to undergraduate and graduate students and young professionals. IEEE Potentials explores career strategies, the latest in research, and important technical developments. Through its articles, it also relates theories to practical applications, highlights technology’s global impact, and generates international forums that foster the sharing of diverse ideas about the profession.

POPULAR ARTICLE

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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