Copertina

Causal Discovery Under Local Privacy

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Binkytė, Rūta, Pinzón, Carlos, Lestyán, Szilvia, Jung, Kangsoo, Arcolezi, Héber H., Palamidessi, Catuscia
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Cryptography and Security Artificial Intelligence Machine Learning Methodology
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
di: Makhlouf, Karima, et al.
Pubblicazione: (2024)

Tight Differential Privacy Guarantees for the Shuffle Model with $k$-Randomized Response
di: Biswas, Sayan, et al.
Pubblicazione: (2022)

Mitigating Membership Inference Vulnerability in Personalized Federated Learning
di: Jung, Kangsoo, et al.
Pubblicazione: (2025)

On the Impact of Multi-dimensional Local Differential Privacy on Fairness
di: Makhlouf, Karima, et al.
Pubblicazione: (2023)

Beyond Epsilon: A Principled QIF Framework for Local Differential Privacy
di: Gonze, Ramon G., et al.
Pubblicazione: (2026)

Protection against Source Inference Attacks in Federated Learning
di: Athanasiou, Andreas, et al.
Pubblicazione: (2026)

Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling
di: Athanasiou, Andreas, et al.
Pubblicazione: (2024)

Jeffrey's update rule as a minimizer of Kullback-Leibler divergence
di: Pinzón, Carlos, et al.
Pubblicazione: (2025)

Private Frequency Estimation Via Residue Number Systems
di: Arcolezi, Héber H.
Pubblicazione: (2025)

Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks
di: Díaz, Judith Sáinz-Pardo, et al.
Pubblicazione: (2025)

BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables
di: Binkyte, Ruta, et al.
Pubblicazione: (2023)

Estimating the True Distribution of Data Collected with Randomized Response
di: Pinzón, Carlos Antonio, et al.
Pubblicazione: (2026)

Distribution-Aware Mean Estimation under User-level Local Differential Privacy
di: Pla, Corentin, et al.
Pubblicazione: (2024)

Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness
di: Arcolezi, Héber H., et al.
Pubblicazione: (2025)

Revisiting Locally Differentially Private Protocols: Towards Better Trade-offs in Privacy, Utility, and Attack Resistance
di: Arcolezi, Héber H., et al.
Pubblicazione: (2025)

Disentangle Estimation of Causal Effects from Cross-Silo Data
di: Liu, Yuxuan, et al.
Pubblicazione: (2024)

Information Leakage Envelopes
di: Saeidian, Sara, et al.
Pubblicazione: (2026)

Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective
di: Arcolezi, Héber H., et al.
Pubblicazione: (2023)

Bayes' capacity as a measure for reconstruction attacks in federated learning
di: Biswas, Sayan, et al.
Pubblicazione: (2024)

Private Prediction Sets
di: Angelopoulos, Anastasios N., et al.
Pubblicazione: (2021)

CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution
di: Kim, Minbeom, et al.
Pubblicazione: (2026)

Self-Defense: Optimal QIF Solutions and Application to Website Fingerprinting
di: Athanasiou, Andreas, et al.
Pubblicazione: (2024)

On the Consistency and Performance of the Iterative Bayesian Update
di: ElSalamouny, Ehab, et al.
Pubblicazione: (2025)

Bayesian Inference Under Differential Privacy With Bounded Data
di: Kazan, Zeki, et al.
Pubblicazione: (2024)

CURATE: Scaling-up Differentially Private Causal Graph Discovery
di: Bhattacharjee, Payel, et al.
Pubblicazione: (2024)

A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System
di: Lin, Zong-Zhi, et al.
Pubblicazione: (2022)

Sequentially Auditing Differential Privacy
di: González, Tomás, et al.
Pubblicazione: (2025)

Decoupling Generalizability and Membership Privacy Risks in Neural Networks
di: Fang, Xingli, et al.
Pubblicazione: (2026)

Privacy Preserving Adaptive Experiment Design
di: Li, Jiachun, et al.
Pubblicazione: (2024)

Learnability and Privacy Vulnerability are Entangled in a Few Critical Weights
di: Fang, Xingli, et al.
Pubblicazione: (2026)

Auditing Differential Privacy in the Black-Box Setting
di: Shi, Kaining, et al.
Pubblicazione: (2025)

Certified Causal Defense with Generalizable Robustness
di: Qiao, Yiran, et al.
Pubblicazione: (2024)

Differentially Private Covariate Balancing Causal Inference
di: Ohnishi, Yuki, et al.
Pubblicazione: (2024)

Bayes Security: A Not So Average Metric
di: Chatzikokolakis, Konstantinos, et al.
Pubblicazione: (2020)

How Tough Is Location Anonymization? Re-identifying 100K Real-User Trajectories in Japan
di: Mishra, Abhishek Kumar, et al.
Pubblicazione: (2025)

A privacy preserving querying mechanism with high utility for electric vehicles
di: Atmaca, Ugur Ilker, et al.
Pubblicazione: (2022)

Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy
di: Kazan, Zeki, et al.
Pubblicazione: (2023)

Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
di: Islam, MD Shafikul, et al.
Pubblicazione: (2026)

Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions
di: Awasthi, Anant Prakash, et al.
Pubblicazione: (2024)

Robust Privacy: Inference-Time Privacy through Certified Robustness
di: Jin, Jiankai, et al.
Pubblicazione: (2026)