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20242026
most citedHow to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy

1 citations · 1 across the 2 of their papers we have counts for

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10 papers

cs.CR20261 cited

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy

Natalia Ponomareva, Zheng Xu, H. Brendan McMahan +12

High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated da…

quant-ph2026

Beyond the Unruh vacuum: multi-time correlations in black hole collapse and evaporation

Konstantinos Xenos, Charis Anastopoulos, Andreas F. Terzis

The black hole information paradox originates from the thermal character of Hawking radiation, which appears to erase information about the collapsing matter. However, thermality c…

cs.CL2025

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

cs.LG2025

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research

A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen +34

"Machine unlearning" is a popular proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons, including privacy, copyri…

cs.LG2025

The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections

Milad Nasr, Nicholas Carlini, Chawin Sitawarin +11

How should we evaluate the robustness of language model defenses? Current defenses against jailbreaks and prompt injections (which aim to prevent an attacker from eliciting harmful…

cs.CR2025

Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks

Milad Nasr, Yanick Fratantonio, Luca Invernizzi +7

As deep learning models become widely deployed as components within larger production systems, their individual shortcomings can create system-level vulnerabilities with real-world…