arXiv:2512. 03238v2 Announce Type: replace-cross Abstract: High quality data is needed to unlock the full potential of AI for end users.
By Natalia Ponomareva, Zheng Xu, H. Brendan McMahan, Peter Kairouz, Lucas Rosenblatt, Vincent Cohen-Addad, Crist\'obal Guzm\'an, Ryan McKenna, Galen Andrew, Alex Bie, Da Yu, Alex Kurakin, Morteza Zadimoghaddam, Sergei Vassilvitskii, Andreas Terzis
Datasette 1.0a39 and 0.65.4 are new security patch releases for the current alpha series and the stable 0.65.x family. The updates address security fixes that are important for public-facing Datasette instances, especially those that mix public and private tables. The patches were developed after an extensive audit using advanced AI models and collaborative review, and the process will be incorporated into future development work.
arXiv:2503. 10945v3 Announce Type: replace-cross Abstract: Current practices for reporting differential privacy (DP) guarantees for machine learning (ML) algorithms such as DP-SGD provide an incomplete and potentially misleading picture.
By Juan Felipe Gomez, Bogdan Kulynych, Georgios Kaissis, Flavio P. Calmon, Jamie Hayes, Borja Balle, Antti Honkela
The paper addresses a flaw in differential privacy audits where selection bias in Membership Inference Attacks leads to overestimated lower bounds on privacy leakage. By applying statistical correction methods, the authors demonstrate that uncorrected audits can violate theoretical privacy guarantees up to four times more often than claimed, while corrected audits maintain valid bounds at the nominal confidence level.
By Valentin Dorseuil (DI-ENS), Meenatchi Sundaram Muthu Selva Annamalai (UCL), Jamal Atif (CMAP), Olivier Capp\'e (DI-ENS), Nicolas Papernot (Inria)
arXiv:2609.36153v1 Announce Type: cross
Abstract: B2B advertising targets a viewer's professional attributes (employer size and industry, function, seniority) and has obtained them by matching identi...
By Om Shankar Tiwari, Navnit Shukla, Guanyu Wang, Akshay Jain
arXiv:2609.38934v1 Announce Type: cross
Abstract: Differentially private (DP) text generation can protect individual records, but privacy alone does not specify what evidence a released statement car...
By Tsubasa Takahashi, Takumi Hiraoka