Large Language Models (LLMs) represent a significant advancement in artificial
intelligence, finding applications across various domains. However, their reliance on …

Across academia, government, and industry, data stewards are facing increasing pressure
to make datasets more openly accessible for researchers while also protecting the privacy of …

Differential privacy (DP) has the potential to enable privacy-preserving analysis on sensitive
data, but requires analysts to judiciously spend a limited" privacy loss budget" ϵ across …

Differential privacy (DP) has become the gold standard in privacy-preserving data analytics,
but implementing it in realworld datasets and systems remains challenging. Recently …

The Digital Markets Act (DMA) constitutes a crucial part of the European legislative
framework addressing the dominance of'Big Tech'. It intends to foster fairness and …

Differential privacy is seen by many experts as the 'gold standard'for privacy-preserving data
analysis. Others argue that while differential privacy is a clean formulation in theory, it is not …

Differential privacy (DP) is a promising framework for privacy-preserving data science, but
recent studies have exposed challenges in bringing this theoretical framework for privacy …

In light of recent growth in privacy awareness and data ownership rights, differential privacy
(DP) has emerged as a promising technique employed by several well-known data …

Information about us, our actions, and our preferences is created at scale through surveys or
scientific studies or as a result of our interaction with digital devices such as smartphones …

As Differential Privacy (DP) transitions from theory to practice, visualization has surfaced as
a catalyst in promoting acceptance and usage. Despite the potential of visualization tools to …