Comparing privacy notions for protection against reconstruction attacks in machine learning
Fuente:
arXiv
Saved in:
| Main Authors: | Biswas, Sayan, Dras, Mark, Faustini, Pedro, Fernandes, Natasha, McIver, Annabelle, Palamidessi, Catuscia, Sadeghi, Parastoo |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bayes' capacity as a measure for reconstruction attacks in federated learning
by: Biswas, Sayan, et al.
Published: (2024)
by: Biswas, Sayan, et al.
Published: (2024)
Empirical Calibration and Metric Differential Privacy in Language Models
by: Faustini, Pedro, et al.
Published: (2025)
by: Faustini, Pedro, et al.
Published: (2025)
Composition Theorems for f-Differential Privacy
by: Fernandes, Natasha, et al.
Published: (2025)
by: Fernandes, Natasha, et al.
Published: (2025)
A privacy preserving querying mechanism with high utility for electric vehicles
by: Atmaca, Ugur Ilker, et al.
Published: (2022)
by: Atmaca, Ugur Ilker, et al.
Published: (2022)
IDT: Dual-Task Adversarial Attacks for Privacy Protection
by: Faustini, Pedro, et al.
Published: (2024)
by: Faustini, Pedro, et al.
Published: (2024)
Tight Differential Privacy Guarantees for the Shuffle Model with $k$-Randomized Response
by: Biswas, Sayan, et al.
Published: (2022)
by: Biswas, Sayan, et al.
Published: (2022)
Mitigating Membership Inference Vulnerability in Personalized Federated Learning
by: Jung, Kangsoo, et al.
Published: (2025)
by: Jung, Kangsoo, et al.
Published: (2025)
Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata
by: Alvim, Mário S., et al.
Published: (2022)
by: Alvim, Mário S., et al.
Published: (2022)
A novel analysis of utility in privacy pipelines, using Kronecker products and quantitative information flow
by: Alvim, Mário S., et al.
Published: (2023)
by: Alvim, Mário S., et al.
Published: (2023)
Beyond Theoretical Bounds: Empirical Privacy Loss Calibration for Text Rewriting Under Local Differential Privacy
by: Li, Weijun, et al.
Published: (2026)
by: Li, Weijun, et al.
Published: (2026)
The Privacy-Utility Trade-off in the Topics API
by: Alvim, Mário S., et al.
Published: (2024)
by: Alvim, Mário S., et al.
Published: (2024)
Protection against Source Inference Attacks in Federated Learning
by: Athanasiou, Andreas, et al.
Published: (2026)
by: Athanasiou, Andreas, et al.
Published: (2026)
A Quantitative Information Flow Analysis of the Topics API
by: Alvim, Mário S., et al.
Published: (2023)
by: Alvim, Mário S., et al.
Published: (2023)
Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling
by: Athanasiou, Andreas, et al.
Published: (2024)
by: Athanasiou, Andreas, et al.
Published: (2024)
Jeffrey's update rule as a minimizer of Kullback-Leibler divergence
by: Pinzón, Carlos, et al.
Published: (2025)
by: Pinzón, Carlos, et al.
Published: (2025)
On the Consistency and Performance of the Iterative Bayesian Update
by: ElSalamouny, Ehab, et al.
Published: (2025)
by: ElSalamouny, Ehab, et al.
Published: (2025)
Beyond Epsilon: A Principled QIF Framework for Local Differential Privacy
by: Gonze, Ramon G., et al.
Published: (2026)
by: Gonze, Ramon G., et al.
Published: (2026)
Self-Defense: Optimal QIF Solutions and Application to Website Fingerprinting
by: Athanasiou, Andreas, et al.
Published: (2024)
by: Athanasiou, Andreas, et al.
Published: (2024)
Information Leakage Envelopes
by: Saeidian, Sara, et al.
Published: (2026)
by: Saeidian, Sara, et al.
Published: (2026)
Graded Suspiciousness of Adversarial Texts to Human
by: Tonni, Shakila Mahjabin, et al.
Published: (2024)
by: Tonni, Shakila Mahjabin, et al.
Published: (2024)
Bayes Security: A Not So Average Metric
by: Chatzikokolakis, Konstantinos, et al.
Published: (2020)
by: Chatzikokolakis, Konstantinos, et al.
Published: (2020)
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
by: Makhlouf, Karima, et al.
Published: (2024)
by: Makhlouf, Karima, et al.
Published: (2024)
Correlation inference attacks against machine learning models
by: Creţu, Ana-Maria, et al.
Published: (2021)
by: Creţu, Ana-Maria, et al.
Published: (2021)
Limits of privacy amplification against non-signalling memory attacks
by: Arnon, Rotem, et al.
Published: (2012)
by: Arnon, Rotem, et al.
Published: (2012)
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2025)
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2025)
On the Impact of Multi-dimensional Local Differential Privacy on Fairness
by: Makhlouf, Karima, et al.
Published: (2023)
by: Makhlouf, Karima, et al.
Published: (2023)
Estimating the True Distribution of Data Collected with Randomized Response
by: Pinzón, Carlos Antonio, et al.
Published: (2026)
by: Pinzón, Carlos Antonio, et al.
Published: (2026)
Requiem for a drone: a machine-learning based framework for stealthy attacks against unmanned autonomous vehicles
by: Kim, Kyo Hyun, et al.
Published: (2024)
by: Kim, Kyo Hyun, et al.
Published: (2024)
Advancing privacy in learning analytics using differential privacy
by: Liu, Qinyi, et al.
Published: (2025)
by: Liu, Qinyi, et al.
Published: (2025)
Causal Discovery Under Local Privacy
by: Binkytė, Rūta, et al.
Published: (2023)
by: Binkytė, Rūta, et al.
Published: (2023)
DLP: towards active defense against backdoor attacks with decoupled learning process
by: Ying, Zonghao, et al.
Published: (2024)
by: Ying, Zonghao, et al.
Published: (2024)
Accountable authentication with privacy protection: The Larch system for universal login
by: Dauterman, Emma, et al.
Published: (2023)
by: Dauterman, Emma, et al.
Published: (2023)
Revisiting the attacker's knowledge in inference attacks against Searchable Symmetric Encryption
by: Damie, Marc, et al.
Published: (2025)
by: Damie, Marc, et al.
Published: (2025)
An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
by: Tran, Hoang, et al.
Published: (2026)
by: Tran, Hoang, et al.
Published: (2026)
Federated learning with differential privacy and an untrusted aggregator
by: Liu, Kunlong, et al.
Published: (2023)
by: Liu, Kunlong, et al.
Published: (2023)
Defending against Backdoor Attacks via Module Switching
by: Li, Weijun, et al.
Published: (2025)
by: Li, Weijun, et al.
Published: (2025)
Detection and classification of DDoS flooding attacks by machine learning method
by: Tymoshchuk, Dmytro, et al.
Published: (2024)
by: Tymoshchuk, Dmytro, et al.
Published: (2024)
Reexamination of the realtime protection for user privacy in practical quantum private query
by: Wei, Chun-Yan, et al.
Published: (2024)
by: Wei, Chun-Yan, et al.
Published: (2024)
Information Density Bounds for Privacy
by: Saeidian, Sara, et al.
Published: (2024)
by: Saeidian, Sara, et al.
Published: (2024)
Shadow defense against gradient inversion attack in federated learning
by: Jiang, Le, et al.
Published: (2025)
by: Jiang, Le, et al.
Published: (2025)
Similar Items
-
Bayes' capacity as a measure for reconstruction attacks in federated learning
by: Biswas, Sayan, et al.
Published: (2024) -
Empirical Calibration and Metric Differential Privacy in Language Models
by: Faustini, Pedro, et al.
Published: (2025) -
Composition Theorems for f-Differential Privacy
by: Fernandes, Natasha, et al.
Published: (2025) -
A privacy preserving querying mechanism with high utility for electric vehicles
by: Atmaca, Ugur Ilker, et al.
Published: (2022) -
IDT: Dual-Task Adversarial Attacks for Privacy Protection
by: Faustini, Pedro, et al.
Published: (2024)