The results show that diffusion models are much less private than prior generative models and that mitigating these vulnerabilities may require new advances in privacy-preserving training.
As organizations adapt their IT infrastructure to abide by the latest privacy regulations, they are faced with a dilemma: efficiency versus flexibility.
Research has revealed a rift between consumer desires and organizational mindsets surrounding data privacy, especially artificial intelligence (AI).
Many companies use deceptive design to hold on to customers, increase sales, or acquire personal data.
Russia had the most breaches overall and France had the highest breach density.
Research on more than 20 apps found that the majority collected large amounts of personal data and shared it with third parties.
The researchers are mapping third-party tracking across the online health ecosystem to see possible implications for ad targeting, credit scores, insurance coverage, and more.
Researchers propose a new privacy framework for IoT devices, dubbed Peekaboo, that gives users control over what data can be collected and shared.
A newpaper argues that many promises around privacy-preserving mechanisms will never be fulfilled and we need to accept these inherent limits.
MIT researchers developed a novel privacy-preserving protocol that could enable an algorithm that provides recommendations to guarantee a user’s privacy.