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Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

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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/10.1109/allerton.2013.6736718openalex-snapshot

Local privacy and statistical minimax rates

inferredcites →https://doi.org/10.1109/focs.2007.4389483openalex-snapshot

Mechanism Design via Differential Privacy

inferredcites →https://doi.org/10.1109/focs.2007.66openalex-snapshot

Mechanism Design via Differential Privacy

inferredcites →https://doi.org/10.1109/focs.2010.12openalex-snapshot

Boosting and Differential Privacy

inferredcites →https://doi.org/10.1109/focs.2013.53openalex-snapshot

Local Privacy and Statistical Minimax Rates

inferredcites →https://doi.org/10.1109/globalsip.2013.6736861openalex-snapshot

Stochastic gradient descent with differentially private updates

inferredcites →https://doi.org/10.1109/sp.2017.35openalex-snapshot

Is Interaction Necessary for Distributed Private Learning?

inferredcites →https://doi.org/10.1561/9781601988195openalex-snapshot

The Algorithmic Foundations of Differential Privacy

inferredcites →https://doi.org/10.29012/jpc.v7i2.649openalex-snapshot

Gradual Release of Sensitive Data under Differential Privacy

inferredcites →https://doi.org/10.48550/arxiv.0912.0071openalex-snapshot

Differentially Private Empirical Risk Minimization

inferredcites →https://doi.org/10.48550/arxiv.1109.0105openalex-snapshot

Differentially Private Online Learning

inferredcites →https://doi.org/10.48550/arxiv.1605.08294openalex-snapshot

Privacy Odometers and Filters: Pay-as-you-Go Composition

inferredcites →https://doi.org/10.5281/zenodo.3371773openalex-snapshot

Code for "Accuracy First: Selecting a Differentially Private Level for Accuracy-Constrained ERM

What points at this work

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

inferred← backgroundhttps://doi.org/10.5281/zenodo.3371774zenodo-depositor-declared

inferred← citeshttps://doi.org/10.1016/j.cose.2023.103297openalex-snapshot

A comprehensive review on deep learning algorithms: Security and privacy issues

inferred← citeshttps://doi.org/10.1109/access.2020.3013157openalex-snapshot

DPARM: Differentially Private Association Rules Mining

inferred← citeshttps://doi.org/10.1109/mci.2020.2976185openalex-snapshot

A Survey on Differentially Private Machine Learning [Review Article]

inferred← citeshttps://doi.org/10.1109/mnet.2019.1800362openalex-snapshot

Enabling Secure Intelligent Network with Cloud-Assisted Privacy-Preserving Machine Learning

inferred← citeshttps://doi.org/10.1145/3658644.3670394openalex-snapshot

ProBE: Proportioning Privacy Budget for Complex Exploratory Decision Support

inferred← citeshttps://doi.org/10.1162/99608f92.5d9b1a8dopenalex-snapshot

What Will It Take to Get to Acceptable Privacy-Accuracy Combinations?

inferred← citeshttps://doi.org/10.14778/3467861.3467864openalex-snapshot

Optimizing fitness-for-use of differentially private linear queries

inferred← citeshttps://doi.org/10.14778/3551793.3551821openalex-snapshot

MIDE

inferred← citeshttps://doi.org/10.29012/jpc.731openalex-snapshot

Editorial for Volume 9 Issue 2

inferred← citeshttps://doi.org/10.56553/popets-2022-0070openalex-snapshot

Privacy accounting εconomics: Improving differential privacy composition via a posteriori bounds

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.