arXiv AI October 5, 2026 By Donghwan Kim, Xin Gu, Jinho Baek, Timothy Lo, Younghoon Min, Kwangsik Shin, Jongryool Kim, Jongse Park, Kiwan Maeng

Cocoon: A System Architecture for Differentially Private Training with Correlated Noises

Read the original on arXiv AI →

Cocoon is a system architecture designed to enable efficient differentially private training by managing correlated noise across training iterations. The framework stores and processes large noise histories across CPU, GPU, and a memory extension module, optimizes sparse embedding tables, and utilizes near‑memory processing devices. Experiments on an FPGA‑based NMP prototype show performance improvements ranging from 1.23× to 10.82× compared to existing approaches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 27

Correlating Cross-Iteration Noise for DP-SGD using Model Curvature

arXiv:2510. 05416v3 Announce Type: replace Abstract: Differentially private stochastic gradient descent (DP-SGD) offers the promise of training deep learning models while mitigating many privacy risks.

By Xin Gu, Yingtai Xiao, Guanlin He, Jiamu Bai, Daniel Kifer, Kiwan Maeng

safety

arXiv AI

Jul 9

An Adaptive Differentially Private Federated Learning Framework

arXiv:2602. 06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy.

By Jin Wang, Hui Ma, Yajun Zhang, Xinjun Pei, Ming Yan, Fei Xing, Yikun Chen

safety

arXiv AI

Aug 18

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy

arXiv:2509. 10691v3 Announce Type: replace-cross Abstract: Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensitive information through inversion, reconstruction, and membership inference attacks.

By Fardin Jalil Piran, Zhiling Chen, Yang Zhang, Qianyu Zhou, Jiong Tang, Farhad Imani

llms efficiency benchmarks safety

arXiv AI

Sep 15

SpliTEE: Improving LLM Inference on Trusted Hardware with Differentially Private GPU Outsourcing

SpliTEE extends the split‑inference architecture of Slalom to large language models by protecting intermediate GPU computations with differential privacy rather than encryption. The authors show that masking intermediate representations is essential, as a prompt‑reconstruction attack can recover prompts with about 80% accuracy. Their global sensitivity analysis bounds the noise needed, and they demonstrate that SpliTEE on Intel TDX achieves near‑double the speed of fully CPU‑based inference and outperforms encryption‑based Slalom while maintaining higher accuracy.

By Shashie Dilhara Batan Arachchige, Robin Carpentier, Hassan Jameel Asghar, Dali Kaafar

llms efficiency safety

arXiv AI

1d ago

SpliTEE: Fast and Private LLM Inference by Coupling GPU-Assisted Trusted Execution Environments with Differential Privacy

arXiv:2609.15039v3 Announce Type: replace-cross Abstract: User prompts provided to large language models (LLMs) may contain private information. One way to protect them is to execute the LLM inside a...

By Shashie Dilhara Batan Arachchige, Robin Carpentier, Hassan Jameel Asghar, Dali Kaafar

llms efficiency safety

arXiv Machine Learning

Jul 7

GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning

arXiv:2412. 12640v2 Announce Type: replace Abstract: The increasing demand for data privacy, alongside the benefits of aggregating data from networked devices, has catalyzed the emergence of federated learning (FL).

By Rui Zhang, Ka-Ho Chow

llms safety

One email a morning, machine-written

Related stories

Correlating Cross-Iteration Noise for DP-SGD using Model Curvature

An Adaptive Differentially Private Federated Learning Framework

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy

SpliTEE: Improving LLM Inference on Trusted Hardware with Differentially Private GPU Outsourcing

SpliTEE: Fast and Private LLM Inference by Coupling GPU-Assisted Trusted Execution Environments with Differential Privacy

GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning