arXiv Machine Learning July 27, 2026 By Carter Luck, Olive Franzese-McLaughlin, Elisaweta Masserova, Akira Takahashi, Antigoni Polychroniadou, Nicolas Papernot

Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification

Read the original on arXiv Machine Learning →

arXiv:2607. 21839v1 Announce Type: cross Abstract: Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing their internals or training data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

Jun 15

Behavioral Audit of Machine Unlearning Has a Privacy Cost

arXiv:2606. 14518v1 Announce Type: new Abstract: The removal of learned data from Machine Learning models through Machine Unlearning (MU) has been widely studied; however, there has yet to be an agreed-upon scheme for auditing MU.

By Liou Tang, James Joshi, Ashish Kundu

safety

arXiv Machine Learning

Aug 6

Manipulation-Proof Oblivious Audits against Deceptive Model Providers

arXiv:2608. 04365v1 Announce Type: new Abstract: Audits have emerged as a critical instrument for algorithmic governance, providing a mechanism for external scrutiny and governance of machine learning models.

By Augustin Godinot, Sofiane Azogagh, Julien Ferry, S\'ebastien Gambs

robotics safety

arXiv Machine Learning

Jul 8

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates

arXiv:2604. 04738v2 Announce Type: replace-cross Abstract: Fine-tuning is the dominant paradigm for adapting large machine learning models, yet current deployment pipelines provide no way to verify how a released model was updated.

By Zhenhang Shang, Yingzhe Yu, Kani Chen

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 24

Natural Identifiers for Privacy and Data Audits in Large Language Models

arXiv:2606. 24408v1 Announce Type: new Abstract: Assessing the privacy of large language models (LLMs) presents significant challenges.

By Lorenzo Rossi, Bart{\l}omiej Marek, Franziska Boenisch, Adam Dziedzic

llms safety

arXiv AI

Jul 22

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data

arXiv:2606. 16952v2 Announce Type: replace-cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.

By Kareem Amin, Rudrajit Das, Alessandro Epasto, Adel Javanmard, Dennis Kraft, M\'onica Ribero, Sergei Vassilvitskii

llms safety

arXiv Machine Learning

Jul 10

Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives

arXiv:2607. 07762v1 Announce Type: new Abstract: Modern machine learning (ML) increasingly relies on complex models whose behavior is difficult to characterize beyond empirical performance metrics.

By Thibaut Vidal, Julien Ferry

efficiency safety

Related stories

Behavioral Audit of Machine Unlearning Has a Privacy Cost

Manipulation-Proof Oblivious Audits against Deceptive Model Providers

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates

Natural Identifiers for Privacy and Data Audits in Large Language Models

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data

Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives