Privacy

Machine learning

Secure Communication in Self-Driving Cars: New Protocol Offers Improved Security and Performance

A team of researchers has proposed an anonymous and provably secure lightweight authentication protocol for unmanned aerial vehicle-assisted internet of autonomous vehicles (IoAVs). The protocol, called SLAP-IoAV, uses exclusive-OR operations, elliptic-curve cryptography, collision-resistant one-way hashing, and concatenation to ensure robust security. The researchers found that SLAP-IoAV is secure against several

Machine learning

Physics-Informed Graph Neural Networks for Attack Path Prediction

Research conducted by University Paris-Dauphine has explored the application of physics-informed graph neural networks (PIGNN) in predicting attack paths within complex infrastructure systems. The study aimed to address the limitations of existing methods, such as machine learning (ML), which struggle to predict full attack paths due to inadequate datasets and

Machine learning

University of Westminster's Professor Tamas Kiss Showcases Privacy-Preserving Machine Learning Techniques at European Security Research Event

Professor Tamas Kiss, a renowned expert in distributed computing, recently attended the European Security Research Event in Warsaw, Poland, to present his project, Harpocrates, to Commissioner Magnus Brunner of the European Union's Internal Affairs and Migration. The Harpocrates project, funded by the European Commission and UK Research and

Machine learning

Secure and Intelligent Energy Markets: Blockchain-Based Peer-to-Peer Energy Trading Framework

A new study published in the Journal of Internet of Things has proposed a secure and intelligent peer-to-peer energy trading framework called zkPET. The framework integrates machine learning and blockchain with advanced cryptographic techniques to protect user data while enabling intelligent decision-making. According to the researchers, zkPET addresses significant challenges

Machine learning

Researchers Uncover Vulnerabilities in Dimensionality Reduction Methods

Research conducted at Chiang Mai University has revealed weaknesses in the security of dimensionality reduction methods, a crucial aspect of information technology and information security. The study focused on machine learning-based reconstruction attacks, which can potentially breach user privacy. Key findings indicate that certain dimensionality reduction techniques, such as principal

Machine learning

AI-Powered Wearable Devices Raise Ethical and Regulatory Concerns

Researchers at the University of Oxford have detailed new data on the integration of artificial intelligence (AI) and machine learning (ML) into wearable sensor technologies. According to the study, the rapid advancement of these technologies has raised concerns around data privacy, algorithmic bias, informed consent, and the opacity of automated

Machine learning

Federated Learning Proves Promising for Human Factors Research

A new study from North Carolina State University has demonstrated the efficacy of federated learning for human factors research, providing a promising alternative to traditional centralized machine learning methods that often encounter critical data privacy issues. The research aimed to develop a privacy-preserving federated learning framework for two specific human

Machine learning

Distributed Machine Learning in the Metaverse: A Contemporary Review of Privacy-Preserving Concerns

Researchers from Sejong University have conducted an in-depth analysis of the intersection of distributed machine learning and the metaverse, highlighting several potential benefits and significant privacy concerns. The study, published in the May 2025 issue of ICT Express, emphasizes the need for privacy-preserving measures in the development of metaverse applications.

Machine learning

Zero Trust Model Enhances Security in Federated Learning Networks

Research conducted at the Royal Melbourne Institute of Technology - RMIT University has revealed a novel framework that leverages the Zero Trust model to identify and counter external threats in Federated Learning (FL) networks. The study proposes a framework strengthened by the Zero Trust model to identify external free riders

Machine learning

Researchers Develop Novel AI Model for Diabetes Management

A team of researchers from North Dakota State University has made significant strides in developing a novel artificial intelligence (AI) model for enhancing diabetes management with privacy-preserving edge AI. According to the study, the increasing prevalence of diabetes necessitates innovative glucose prediction methods that prioritize patient privacy. The researchers introduced

Social media

US Embassy in Dhaka Requests Social Media Accounts Be Set to Public for Visa Vetting

The US Embassy in Dhaka has requested that Bangladeshi applicants for student and exchange programme visas adjust their social media account privacy settings to 'public' to facilitate the visa vetting process. The guidance follows a recent announcement by the US Department of State confirming the resumption of student

Social media

Italian Data Protection Authority Restricts Employer Use of Social Media Evidence in Disciplinary Proceedings

The Italian Data Protection Authority has issued a new decision further restricting employers' ability to use information from third-party reports, private conversations, or social media posts as evidence in disciplinary proceedings. This decision adds to the ongoing debate on the subject and introduces additional constraints, particularly regarding the use