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Numerical Composition of Differential Privacy

https://doi.org/10.29012/jpc.870
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11681878_14openalex-snapshot
Calibrating Noise to Sensitivity in Private Data Analysis
inferredcites →https://doi.org/10.1007/11761679_29openalex-snapshot
Our Data, Ourselves: Privacy Via Distributed Noise Generation
inferredcites →https://doi.org/10.1007/978-3-662-49096-9_7openalex-snapshot
The Complexity of Computing the Optimal Composition of Differential Privacy
inferredcites →https://doi.org/10.1007/978-3-662-53641-4_24openalex-snapshot
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds
inferredcites →https://doi.org/10.1109/csf.2017.11openalex-snapshot
Rényi Differential Privacy
inferredcites →https://doi.org/10.1109/focs.2010.12openalex-snapshot
Boosting and Differential Privacy
inferredcites →https://doi.org/10.1109/tit.2017.2685505openalex-snapshot
The Composition Theorem for Differential Privacy
inferredcites →https://doi.org/10.1111/rssb.12454openalex-snapshot
Gaussian Differential Privacy
inferredcites →https://doi.org/10.1145/1536414.1536466openalex-snapshot
Differential privacy and robust statistics
inferredcites →https://doi.org/10.1145/2976749.2978318openalex-snapshot
Deep Learning with Differential Privacy
inferredcites →https://doi.org/10.1145/3188745.3188946openalex-snapshot
Composable and versatile privacy via truncated CDP
inferredcites →https://doi.org/10.1145/3243734.3243765openalex-snapshot
Tight on Budget?
inferredcites →https://doi.org/10.1162/99608f92.cfc5dd25openalex-snapshot
Deep Learning with Gaussian Differential Privacy
inferredcites →https://doi.org/10.1561/9781601988195openalex-snapshot
The Algorithmic Foundations of Differential Privacy
inferredcites →https://doi.org/10.2478/popets-2019-0029openalex-snapshot
Privacy Loss Classes: The Central Limit Theorem in Differential Privacy
inferredcites →https://doi.org/10.4230/lipics.forc.2026.4openalex-snapshot
Concentrated Differential Privacy
inferredcites →https://doi.org/10.48550/arxiv.1805.06530openalex-snapshot
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
inferredcites →https://doi.org/10.48550/arxiv.1906.03049openalex-snapshot
Computing Tight Differential Privacy Guarantees Using FFT
inferredcites →https://doi.org/10.48550/arxiv.2006.07134openalex-snapshot
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
inferredcites →https://doi.org/10.48550/arxiv.2102.03013openalex-snapshot
Fast and Memory Efficient Differentially Private-SGD via JL Projections
inferredcites →https://doi.org/10.48550/arxiv.2102.12412openalex-snapshot
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT
inferredcites →https://doi.org/10.48550/arxiv.2106.08567openalex-snapshot
Optimal Accounting of Differential Privacy via Characteristic Function
inferredcites →https://doi.org/10.48550/arxiv.2207.04381openalex-snapshot
Faster Privacy Accounting via Evolving Discretization
inferredcites →https://doi.org/10.48550/arxiv.2208.09595openalex-snapshot
The Saddle-Point Accountant for Differential Privacy
inferredcites →https://doi.org/10.56553/popets-2022-0122openalex-snapshot
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4906113openalex-extracted
Dp-Sgd-Global-Adapt-V2-S: Triad Improvements of Privacy, Accuracy and Fairness Via Step Decay Noise Multiplier and Step Decay Upper Clipping Threshold
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2026.122175openalex-snapshot
Privacy-preserving visualization of brain functional connectivity
inferred← citeshttps://doi.org/10.1038/s41598-025-16810-yopenalex-snapshot
Privacy-preserving communication in smart city transportation using elliptic curve cryptography
inferred← citeshttps://doi.org/10.1109/cyberc66434.2025.00025openalex-snapshot
ALIGN-FL: Architecture-independent Learning through Invariant Generative component sharing in Federated Learning
inferred← citeshttps://doi.org/10.1109/eurosp63326.2025.00041openalex-snapshot
Sandi: A System for Accountability
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10890461openalex-snapshot
Differentially Private Distribution Estimation Using Functional Approximation
inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11462841openalex-snapshot
On The Sensitivity of Firing Rate-Based Federated Spiking Neural Networks to Differential Privacy
inferred← citeshttps://doi.org/10.1109/iscas66217.2026.11562548openalex-snapshot
Privacy-Preserving Machine Learning Accelerators: Balancing Efficiency and Security
inferred← citeshttps://doi.org/10.1109/isit54713.2023.10206616openalex-snapshot
Schrödinger Mechanisms: Optimal Differential Privacy Mechanisms for Small Sensitivity
inferred← citeshttps://doi.org/10.1109/isit54713.2023.10206658openalex-snapshot
Optimal Multidimensional Differentially Private Mechanisms in the Large-Composition Regime
inferred← citeshttps://doi.org/10.1109/isit57864.2024.10619626openalex-snapshot
Differential-Privacy Capacity
inferred← citeshttps://doi.org/10.1109/isit62367.2026.11653633openalex-snapshot
Composition Theorems for Multiple Differential Privacy Constraints
inferred← citeshttps://doi.org/10.1109/isit63088.2025.11195387openalex-snapshot
Tensorization of $f$-Divergences
inferred← citeshttps://doi.org/10.1109/isit63088.2025.11195526openalex-snapshot
Differential Confounding Privacy and Inverse Composition
inferred← citeshttps://doi.org/10.1109/satml59370.2024.00014openalex-snapshot
PILLAR: How to make semi-private learning more effective
inferred← citeshttps://doi.org/10.1109/satml64287.2025.00027openalex-snapshot
Hyperparameters in Score-Based Membership Inference Attacks
inferred← citeshttps://doi.org/10.1109/satml64287.2025.00060openalex-snapshot
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms Under Composition
inferred← citeshttps://doi.org/10.1109/satml64287.2025.00061openalex-snapshot
Auditing Differential Privacy Guarantees Using Density Estimation
inferred← citeshttps://doi.org/10.1109/satml68715.2026.00033openalex-snapshot
Position: Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
inferred← citeshttps://doi.org/10.1109/sp61157.2025.00217openalex-snapshot
From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis
inferred← citeshttps://doi.org/10.1109/tifs.2024.3516579openalex-snapshot
Balancing Differential Privacy and Utility: A Relevance-Based Adaptive Private Fine-Tuning Framework for Language Models
inferred← citeshttps://doi.org/10.1109/tifs.2025.3557755openalex-snapshot
DC-SGD: Differentially Private SGD With Dynamic Clipping Through Gradient Norm Distribution Estimation
inferred← citeshttps://doi.org/10.1109/tifs.2025.3620213openalex-snapshot
Differentially Private Vertical Federated Learning With Adaptive Constraints and Dynamic Noise
inferred← citeshttps://doi.org/10.1109/tkde.2024.3381832openalex-snapshot
Edge-Protected Triangle Count Estimation Under Relationship Local Differential Privacy
inferred← citeshttps://doi.org/10.1109/tmc.2025.3559621openalex-snapshot
Differential Private Data Stream Analytics in the Local and Shuffle Models
inferred← citeshttps://doi.org/10.1109/tnse.2025.3646341openalex-snapshot
On the Price of Differential Privacy for Spectral Clustering Over Stochastic Block Models
inferred← citeshttps://doi.org/10.1109/trustcom66490.2025.00339openalex-snapshot
PIRL: A Robust and Privacy-Preserving Learning Method from an Information-Theoretic Perspective
inferred← citeshttps://doi.org/10.1109/wacv57701.2024.00475openalex-snapshot
Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection
inferred← citeshttps://doi.org/10.1146/annurev-statistics-112723-034158openalex-snapshot
A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation, and Blackwell's Theorem
inferred← citeshttps://doi.org/10.18653/v1/2022.emnlp-main.323openalex-snapshot
Differentially Private Language Models for Secure Data Sharing
inferred← citeshttps://doi.org/10.18653/v1/2023.acl-long.74openalex-snapshot
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
inferred← citeshttps://doi.org/10.29012/jpc.880openalex-snapshot
Differentially Private Fine-tuning of Language Models
inferred← citeshttps://doi.org/10.3390/app15031595openalex-snapshot
Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2106.08567openalex-snapshot
Optimal Accounting of Differential Privacy via Characteristic Function
inferred← citeshttps://doi.org/10.48550/arxiv.2110.05679openalex-snapshot
Large Language Models Can Be Strong Differentially Private Learners
inferred← citeshttps://doi.org/10.48550/arxiv.2111.00173openalex-snapshot
Dynamic Differential-Privacy Preserving SGD
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