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An information theorist's tour of differential privacy
Anand D. Sarwate, Flavio P. Calmon, Oliver Kosut, Lalitha Sankar
https://arxiv.org/abs/2510.10316 https://

An information theorist's tour of differential privacy
Since being proposed in 2006, differential privacy has become a standard method for quantifying certain risks in publishing or sharing analyses of sensitive data. At its heart, differential privacy measures risk in terms of the differences between probability distributions, which is a central topic in information theory. A differentially private algorithm is a channel between the underlying data and the output of the analysis. Seen in this way, the guarantees made by differential privacy can be…

Age-period modeling of mortality gaps: the cases of cancer and circulatory diseases
Giacomo Lanfiuti Baldi, Andrea Nigri, Han Lin Shang
https://arxiv.org/abs/2510.10904 https://…

Age-period modeling of mortality gaps: the cases of cancer and circulatory diseases
Understanding and modeling mortality patterns, especially differences in mortality rates between populations, is vital for demographic analysis and public health planning. We compare three statistical models within the age-period framework to examine differences in death counts. The models are based on the double Poisson, bivariate Poisson, and Skellam distributions, each of which provides unique strengths in capturing underlying mortality trends. Focusing on mortality data from 1960 to 2015, w…

HALF: Harm-Aware LLM Fairness Evaluation Aligned with Deployment
Ali Mekky, Omar El Herraoui, Preslav Nakov, Yuxia Wang
https://arxiv.org/abs/2510.12217 https://

HALF: Harm-Aware LLM Fairness Evaluation Aligned with Deployment
Large language models (LLMs) are increasingly deployed across high-impact domains, from clinical decision support and legal analysis to hiring and education, making fairness and bias evaluation before deployment critical. However, existing evaluations lack grounding in real-world scenarios and do not account for differences in harm severity, e.g., a biased decision in surgery should not be weighed the same as a stylistic bias in text summarization. To address this gap, we introduce HALF (Harm-A…