Opacus: User-Friendly Differential Privacy Library in PyTorch | Paper Citation Record

Observation ebc6433b-5cb6-42b6-9aea-253078c43cde · inbound

Privacy Leakage via Output Label Space and Differentially Private Continual Learning cites this paper.

Privacy Leakage via Output Label Space and Differentially Private Continual Learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 84

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Observation 27d6b13f-a2a8-4879-aa95-fbdabd7dc508 · inbound

Combining Machine Learning Defenses without Conflicts cites this paper.

Combining Machine Learning Defenses without Conflicts Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 174

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DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization cites this paper.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 37

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Protecting Confidentiality, Privacy and Integrity in Collaborative Learning cites this paper.

Protecting Confidentiality, Privacy and Integrity in Collaborative Learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 2021

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Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training cites this paper.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 19

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Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis cites this paper.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 51

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Balls-and-Bins Sampling for DP-SGD cites this paper.

Balls-and-Bins Sampling for DP-SGD Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 41

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Observation df1406aa-efc0-4b2d-91ca-20a2d1274b4f · inbound

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry cites this paper.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 35

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Observation e334afae-3f00-45db-a1e8-5dc5a5530e93 · inbound

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data cites this paper.

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 65

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Observation 8eb927d6-982f-432a-bfc1-eaa789a6e4d9 · inbound

Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting cites this paper.

Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 2009

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Observation 4f0c9b1b-fd62-4b3b-ae0f-2370c50b9bea · inbound

Comparing privacy notions for protection against reconstruction attacks in machine learning cites this paper.

Comparing privacy notions for protection against reconstruction attacks in machine learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 52

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Observation 9e229d75-e37e-469b-a89c-ff86027d0e34 · inbound

Hyperparameters in Score-Based Membership Inference Attacks cites this paper.

Hyperparameters in Score-Based Membership Inference Attacks Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 44

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Observation 51720231-639b-43d0-9084-8fc4df41e23a · inbound

General-Purpose $f$-DP Estimation and Auditing in a Black-Box Setting cites this paper.

General-Purpose $f$-DP Estimation and Auditing in a Black-Box Setting Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 50

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Observation e351fc81-e4cb-4818-88f0-234af00b7a3b · inbound

Beyond Anonymization: Object Scrubbing for Privacy-Preserving 2D and 3D Vision Tasks cites this paper.

Beyond Anonymization: Object Scrubbing for Privacy-Preserving 2D and 3D Vision Tasks Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 60

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Observation 4e4f63dd-6dcc-4591-a06b-f2a8de502ae4 · inbound

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation cites this paper.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 24

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Observation c27c6d70-14a4-4f42-bff6-9ab15aba4889 · inbound

Towards Trustworthy Federated Learning with Untrusted Participants cites this paper.

Towards Trustworthy Federated Learning with Untrusted Participants Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 70

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Observation 0fc59f3a-57b7-4cd1-b83f-41d78a3b7644 · inbound

Privacy-Preserving Transformers: SwiftKey's Differential Privacy Implementation cites this paper.

Privacy-Preserving Transformers: SwiftKey's Differential Privacy Implementation Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 13

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Observation 98f2395c-66de-456c-bb2f-6fe0c56d503a · inbound

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs cites this paper.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 23

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Observation e3968089-bdcd-4e9b-968a-9028aae86aa0 · inbound

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning cites this paper.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 60

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Observation b8d97bf0-9dd6-4afa-aa7d-3390d31f67f1 · inbound

Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI cites this paper.

Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 34

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Observation 8a782b08-c043-4001-a549-aa508a3e5f12 · inbound

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches cites this paper.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 2017

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Observation e59bdb1d-de12-4a1d-be47-036607547270 · inbound

Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping cites this paper.

Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 32

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Observation 380618aa-20e5-4ba1-9f0c-1ad070b497d9 · inbound

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning cites this paper.

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 42

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Observation f47e21f8-dabc-463e-ba55-dbcd91d2b39a · inbound

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation? cites this paper.

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation? Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 40

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Observation 087ba672-32dc-4ea9-a0dd-1522d918c573 · inbound

Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing cites this paper.

Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 22

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Observation 338df136-f224-4ce4-9b96-bbda16078be5 · inbound

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction cites this paper.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 74

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Observation aedfebe5-17e8-4b70-9137-578e5c81bb0b · inbound

FlashDP: Private Training Large Language Models with Efficient DP-SGD cites this paper.

FlashDP: Private Training Large Language Models with Efficient DP-SGD Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 52

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Observation 2ff7ad96-94d3-44c7-9a1b-023e54f12e6b · inbound

Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEs cites this paper.

Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEs Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 31

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Observation 9521abe8-326d-46e4-ba6d-45531a6ee02c · inbound

Improving Noise Efficiency in Privacy-preserving Dataset Distillation cites this paper.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 34

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Observation bd885526-567d-45e3-bbed-0c821437e19a · inbound

Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning cites this paper.

Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 174

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Observation 8aad5a5d-0f6f-4515-b290-ac59be85ee8f · inbound

Private Hyperparameter Tuning with Ex-Post Guarantee cites this paper.

Private Hyperparameter Tuning with Ex-Post Guarantee Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 30

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Observation 9c803bdc-7e63-4a2a-8c74-17452adef764 · inbound

Achieving Hilbert-Schmidt Independence Under R\'enyi Differential Privacy for Fair and Private Data Generation cites this paper.

Achieving Hilbert-Schmidt Independence Under R\'enyi Differential Privacy for Fair and Private Data Generation Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 30

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Observation 04c0a7ec-340d-40c9-8205-668454718ef8 · inbound

On the MIA Vulnerability Gap Between Private GANs and Diffusion Models cites this paper.

On the MIA Vulnerability Gap Between Private GANs and Diffusion Models Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 28

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Observation d35c5015-eb39-4b8a-89fc-d0badfdc22ba · inbound

DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling cites this paper.

DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:11:46.563669Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c8e5d791-0473-44a4-81d8-34edc2fcbfe7 · inbound

An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy cites this paper.

An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 66

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Observation 7ff7f96e-fd24-4e74-a612-645367406a0b · inbound

Network-Aware Differential Privacy cites this paper.

Network-Aware Differential Privacy Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 2021

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Observation f0c769f1-01bf-45c3-9513-cbd21a02f3d2 · inbound

On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy cites this paper.

On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 40

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Observation d905cf6d-215d-4442-93a8-655192c64173 · inbound

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection cites this paper.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 50

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Observation d1171136-0d3e-483f-a5b5-5f2f0df81da2 · inbound

Term2Note: Synthesising Differentially Private Clinical Notes from Medical Terms cites this paper.

Term2Note: Synthesising Differentially Private Clinical Notes from Medical Terms Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 41

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Observation bc60db6b-6dd5-445b-a16e-5edc69a925ef · inbound

Correlating Cross-Iteration Noise for DP-SGD using Model Curvature cites this paper.

Correlating Cross-Iteration Noise for DP-SGD using Model Curvature Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 57

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Observation 32e806b9-3789-4f1f-88b0-fa413bd81ea2 · inbound

On Optimal Hyperparameters for Differentially Private Deep Transfer Learning cites this paper.

On Optimal Hyperparameters for Differentially Private Deep Transfer Learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T04:40:52.723676Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1101091f-d5ed-40dd-a1c7-055d0b639974 · inbound

Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy cites this paper.

Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-17T04:41:31.412929Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f79e3bd8-5544-4aec-a033-a809d7b884e2 · inbound

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

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 264

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Observation fa1ea77f-bdad-4128-af95-67b93fc1d251 · inbound

FedVideoMAE: Efficient Privacy-Preserving Federated Video Moderation cites this paper.

FedVideoMAE: Efficient Privacy-Preserving Federated Video Moderation Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-16T20:38:24.735067Z

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Observation 6859cfc2-5142-4119-91c5-dbfd3278a174 · inbound

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD cites this paper.

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-16T13:37:56.477863Z

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Observation acbb4db8-e5d1-4460-82df-8f873ab9ee41 · inbound

Composition for Pufferfish Privacy cites this paper.

Composition for Pufferfish Privacy Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 54

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Observation 87057a70-3025-4f0d-bcd8-895ea34c133b · inbound

PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems cites this paper.

PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 59

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Observation d9fdaad6-36d6-4633-8801-1d35654c66f4 · inbound

A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning cites this paper.

A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 50

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Observation 7c1afa2e-b222-41c9-a999-029a85ce690d · inbound

Differentially Private Modeling of Disease Transmission within Human Contact Networks cites this paper.

Differentially Private Modeling of Disease Transmission within Human Contact Networks Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:56:00.865908Z

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Observation bc6a516e-1726-4045-9a7a-d7c81abd91f0 · inbound

Secure and Privacy-Preserving Vertical Federated Learning cites this paper.

Secure and Privacy-Preserving Vertical Federated Learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 97

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:45:27.749919Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b99c5807-6bcd-4b20-9849-fdd928a6217a · inbound

DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy cites this paper.

DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:58:13.014235Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 672fc446-bdef-4b52-b2ff-8fd5710fb60d · inbound

DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy cites this paper.

DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-21T00:53:53.616825Z

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Observation acf8d866-923e-4750-884f-db22d489ca5d · inbound

Differentially Private Model Merging cites this paper.

Differentially Private Model Merging Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:41:05.239870Z

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Observation 91b10ef9-5c03-4950-84e8-8a9cc0bbfd55 · inbound

Differentially Private Contrastive Learning via Bounding Group-level Contribution cites this paper.

Differentially Private Contrastive Learning via Bounding Group-level Contribution Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:06:25.014380Z

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Observation 898409bc-d5c9-4834-aace-466b02a884ee · inbound

Class-Aware Adaptive Differential Privacy in Deep Learning for Sensor-Based Fall Detection cites this paper.

Class-Aware Adaptive Differential Privacy in Deep Learning for Sensor-Based Fall Detection Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:00.123335Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cafbc604-3b07-4900-aead-9c8586204e1b · inbound

Private Speech Classification without Collapse: Stabilized DP Training and Offline Distillation cites this paper.

Private Speech Classification without Collapse: Stabilized DP Training and Offline Distillation Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:26:13.762807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ff565336-9975-495c-be32-023ae1f36694 · inbound

FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction cites this paper.

FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:41:30.499803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c4bc710e-7062-4738-a619-26b8377d91ad · inbound

Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning cites this paper.

Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:41:09.230745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7cc8288f-1c82-4099-ac2d-2e4bd6d42d8b · inbound

PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization cites this paper.

PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:06:12.174878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f917f902-a8ce-404c-878b-cb5b51619ae7 · inbound

PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization cites this paper.

PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:25:45.405882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 791e950c-6ec7-42b6-9011-517cfed5e2de · inbound

FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics cites this paper.

FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:28:21.410756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation de73cf17-4079-4fdd-837e-08bc4d518526 · inbound

FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics cites this paper.

FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:05:00.933597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 23578c6f-a480-477f-8674-e1536ec32baa · inbound

An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees cites this paper.

An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:09:46.326541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e3aa8bac-fb44-49df-8ea2-37c277a277f5 · inbound

Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy cites this paper.

Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-22T09:11:21.075174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 362f84be-6d7a-4fc0-9028-0d7deab001e9 · inbound

Efficient DP-SGD for LLMs with Randomized Clipping cites this paper.

Efficient DP-SGD for LLMs with Randomized Clipping Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:34:39.074871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 74a6729d-2ee0-42ea-9de4-bf5c86d1e0a9 · inbound

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks cites this paper.

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:14:02.133488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c8cf524e-42ec-432d-b1f6-02423638fef3 · inbound

Fair Finetuning Mitigates Distribution Inference Attacks cites this paper.

Fair Finetuning Mitigates Distribution Inference Attacks Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:56:15.472547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1a8f48ec-924a-4e19-8bf5-1958200e1070 · inbound

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices cites this paper.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:08:02.964719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 40aba182-18f9-4d4f-a0bf-43d3fc5a4674 · inbound

Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy cites this paper.

Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-07-08T10:14:52.065335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 888a603f-4ad9-4b54-810d-5944ba3bdbf7 · inbound

Reducing Per-Sample Interference in Stochastic Optimization cites this paper.

Reducing Per-Sample Interference in Stochastic Optimization Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-02T09:44:40.753808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a34267c1-6f2d-469c-980c-eb164a627db0 · inbound

End-to-End Differential Privacy in Training Deep Neural Network Classifiers cites this paper.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-01T12:29:21.244346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers