arXiv Machine Learning June 16, 2026 By Hanghang Zheng, Xiwei Zhuang, Zhong Wang, Hong Liu, Xiao Chen, Jingwen He, Xia Li

Causal-Privacy Audit Workflow for Synthetic and Distilled Data in Dropout Support

Read the original on arXiv Machine Learning →

arXiv:2606. 15940v1 Announce Type: new Abstract: Synthetic and distilled student data are increasingly used to enable privacy-conscious learning analytics, yet their suitability for decision-facing institutional support remains uncertain.

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

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arXiv AI

Jul 21

Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning

arXiv:2607. 16620v1 Announce Type: cross Abstract: Differential privacy (DP) is increasingly deployed to limit membership inference risk in machine-learning systems.

By Rakshit Naidu

nlp benchmarks safety

arXiv Machine Learning

Jul 10

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

arXiv:2607. 08122v1 Announce Type: new Abstract: Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records.

By Amir Asiaee, Kaveh Aryan

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arXiv AI

Jun 16

Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data

arXiv:2606. 16952v1 Announce Type: 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

Jun 2

Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning

arXiv:2606. 00986v1 Announce Type: new Abstract: Federated learning (FL) enables multiple data holders to train machine learning models collaboratively without centralizing raw data, making it useful in privacy sensitive domains such as healthcare and institutional data sharing.

By Ivo Osterberg Nilsson, Maximilian Birr Engvall, Viktor Valadi, Teddy Lazebnik

llms benchmarks safety

arXiv Machine Learning

Jun 18

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

arXiv:2502. 17748v4 Announce Type: replace Abstract: Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks.

By Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

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Hugging Face Trending Papers

Jul 8

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data

Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness. Differential Privacy (DP) has become a gold standard for privacy-preserving data analysis, while fairness-aware mechanisms aim to mitigate discrimination against underrepresented groups.

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Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data

Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data