Tag: Data Privacy

Researchers have unveiled SENNA, a framework that combines disentangled semantic representations with large language models to generate synthetic tabular data ...

Researchers have developed two federated learning algorithms, Bi-Models Training and Multi-Models Training, that remove a client's influence from a shared ...

A new Cluster Computing study using stroke prediction shows that federated learning in healthcare will fail unless it confronts severe ...

A new federated learning framework called FedGPA balances global generalization and client personalization by aggregating neural network layers at different ...

An open specification and JSON schema called SLPT aims to make learner–AI interaction records portable and machine-checkable, while explicitly withholding ...

Researchers at Donghua University have developed RFLDefense, a layer-wise defense framework that suppresses backdoor attacks in federated learning even when ...

A survey of 300 Jordanian community pharmacists finds that 52 percent are willing to use AI for medication counseling, driven ...

Researchers have developed LLMDPA, an AI-powered framework that audits DP-SGD implementations in source code before training begins, catching broken privacy ...

A PLOS Medicine editorial argues that smartwatch data could build a globally representative cardiovascular research database, but only if ethical, ...

A comprehensive review warns that conversational AI agents pose systemic privacy risks, from memorized personal data and prompt injection attacks ...