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Privacy

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A study on innovation resistance of artificial intelligence voice assistants based on privacy infringement and risk perception.

PloS one
As a vital tool for human-computer interaction, artificial intelligence (AI) voice assistants have become an integral part of individuals' everyday routines. However, there are still a series of problems caused by privacy violations in current use. T...

Model interpretability on private-safe oriented student dropout prediction.

PloS one
Student dropout is a significant social issue with extensive implications for individuals and society, including reduced employability and economic downturns, which, in turn, drastically influence social sustainable development. Identifying students ...

Federated transfer learning with differential privacy for multi-omics survival analysis.

Briefings in bioinformatics
Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of features is significantly larger than the sample size, making the integration of multi-omics data for survival analysis of a specific cancer particularly ...

Retinal imaging in an era of open science and privacy protection.

Experimental eye research
Artificial intelligence (AI) holds great promise for analyzing complex data to advance patient care and disease research. For example, AI interpretation of retinal imaging may enable the development of noninvasive retinal biomarkers of systemic disea...

The INNOVATE framework to foster ethics of artificial intelligence.

Recenti progressi in medicina
ChatGPT, the latest advancement in Artificial Intelligence (AI), represents one of the most advanced and rapidly evolving chatbot technologies to date. Its capability to provide swift and intelligent responses has garnered admiration from scientists ...

PrivCore: Multiplication-activation co-reduction for efficient private inference.

Neural networks : the official journal of the International Neural Network Society
The marriage of deep neural network (DNN) and secure 2-party computation (2PC) enables private inference (PI) on the encrypted client-side data and server-side models with both privacy and accuracy guarantees, coming at the cost of orders of magnitud...

A combined approach of evolutionary game and system dynamics for user privacy protection in human intelligence interaction.

Scientific reports
The rapid development of generative artificial intelligence (GenAI) has generated significant economic and social value, alongside risks to user privacy. For this purpose, this study investigates privacy protection in human-AI interaction by employin...

Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity.

Scientific reports
In the digital age, privacy preservation is of paramount importance while processing health-related sensitive information. This paper explores the integration of Federated Learning (FL) and Differential Privacy (DP) for breast cancer detection, lever...

Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings.

Scientific reports
Ensuring data privacy in medical image classification is a critical challenge in healthcare, especially with the increasing reliance on AI-driven diagnostics. In fact, over 30% of healthcare organizations globally have experienced a data breach in th...

Context-Contingent Privacy Concerns and Exploration of the Privacy Paradox in the Age of AI, Augmented Reality, Big Data, and the Internet of Things: Systematic Review.

Journal of medical Internet research
BACKGROUND: Despite extensive research into technology users' privacy concerns, a critical gap remains in understanding why individuals adopt different standards for data protection across contexts. The rise of advanced technologies such as the Inter...