IEEE Technology and Society Magazine最新文献_第5页

A Conversation on Waste and Local Action 废物与地方行动对话

IF 2.1 4区工程技术

IEEE Technology and Society Magazine Pub Date : 2025-03-30 DOI: 10.1109/MTS.2025.3565029

Ketra Schmitt;Faisal Shennib;Anna Timm-Bottos;Arrien Weeks

引用次数: 0

A Day Trip Through Technology Choices 科技选择一日游

IF 2.1 4区工程技术

IEEE Technology and Society Magazine Pub Date : 2025-03-30 DOI: 10.1109/MTS.2025.3560347

Ketra Schmitt

引用次数: 0

Smart AI, Private Lives: Can We Have Both? 智能人工智能与私人生活：我们能两者兼得吗？

IF 2.1 4区工程技术

IEEE Technology and Society Magazine Pub Date : 2025-03-30 DOI: 10.1109/MTS.2025.3563037

Murty Polavarapu

引用次数: 0

IEEE Foundation IEEE基金会

IF 2.1 4区工程技术

IEEE Technology and Society Magazine Pub Date : 2025-03-30 DOI: 10.1109/MTS.2025.3566129

引用次数: 0

Real Is Beautiful: Selfies, Social Media, and Self-Determination 真实即美丽：自拍、社交媒体和自我决定

IF 2.1 4区工程技术

IEEE Technology and Society Magazine Pub Date : 2025-03-30 DOI: 10.1109/MTS.2025.3549210

Katina Michael;M. G. Michael

引用次数: 0

IEEE SSIT IEEE SSIT

IF 2.1 4区工程技术

IEEE Technology and Society Magazine Pub Date : 2025-03-30 DOI: 10.1109/MTS.2025.3562472

引用次数: 0

Self-Efficacy, Innovativeness, and Their Influence on Cybersecurity Compliance Intentions Among Public-Sector Employees in Sierra Leone 塞拉利昂公共部门员工自我效能感、创新能力及其对网络安全合规意愿的影响

IF 2.1 4区工程技术

IEEE Technology and Society Magazine Pub Date : 2025-03-28 DOI: 10.1109/MTS.2025.3550791

Mohamed Koroma;Jinfu Chen;Ibrahim Abdulai Sawaneh;Rexford Nii Ayitey Sosu

引用次数: 0

Privacy-Preserving Federated Learning for Phishing Detection 保护隐私的网络钓鱼检测联合学习

IF 2.1 4区工程技术

IEEE Technology and Society Magazine Pub Date : 2025-03-08 DOI: 10.1109/MTS.2025.3558971

Amr I. Elkhawas;Thomas M. Chen;Ilir Gashi

{"title":"Privacy-Preserving Federated Learning for Phishing Detection","authors":"Amr I. Elkhawas;Thomas M. Chen;Ilir Gashi","doi":"10.1109/MTS.2025.3558971","DOIUrl":"https://doi.org/10.1109/MTS.2025.3558971","url":null,"abstract":"Machine learning is one of the most prominent technologies used to combat phishing detection; however, the vast amount of data required for training models for detection raises a privacy concern for end users. Gathering email or document data may very well contain private information, and the machine learning models learn from the words and other attributes of these text-based documents. Gathering this information in a centralized location and using it to train models could pose a security risk on all levels of data acquisition, from the transfer of the data to the storage. Federated learning is emerging as a promising alternative to traditionally centralized machine learning for phishing detection. The advantages of federated learning, mainly in privacy and scalability, are weighed against the issue of detection accuracy. Federated learning provides the ability to train models without the transfer of sensitive data, more or less no raw data from the device, and allows the training to be done locally; this eliminates the privacy exposure accompanied by traditional machine learning models that operate in a centralized manner. However, this alone is not enough to comply with privacy regulations, such as General Data Protection Regulation (GDPR) and the European Union (EU) Artificial Intelligence Act (AI Act), and privacy-preserving technology must be used in conjunction to ensure federated learning’s compliance with privacy regulations. This article is a dedication to Professor Thomas Chen’s aspirations in the field of cybersecurity. This article is dedicated to his memory.","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":"44 2","pages":"77-84"},"PeriodicalIF":2.1,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Role of Protocol Papers, Scoping Reviews, and Systematic Reviews in Responsible AI Research 协议文件、范围审查和系统审查在负责任的人工智能研究中的作用

IF 2.1 4区工程技术

IEEE Technology and Society Magazine Pub Date : 2025-03-07 DOI: 10.1109/MTS.2025.3558666

Calvin Hillis;Ebrahim Bagheri;Zack Marshall

引用次数: 0

We Want to Hear From You! 我们想听到你的声音！

IF 2.1 4区工程技术

IEEE Technology and Society Magazine Pub Date : 2025-03-05 DOI: 10.1109/MTS.2025.3544951

引用次数: 0