Copertina

Improving ML Attacks on LWE with Data Repetition and Stepwise Regression

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Alfarano, Alberto, Saxena, Eshika, Wenger, Emily, Charton, François, Lauter, Kristin
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Cryptography and Security Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

TAPAS: Datasets for Learning the Learning with Errors Problem
di: Saxena, Eshika, et al.
Pubblicazione: (2025)

Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors
di: Stevens, Samuel, et al.
Pubblicazione: (2024)

Benchmarking Attacks on Learning with Errors
di: Wenger, Emily, et al.
Pubblicazione: (2024)

The cool and the cruel: separating hard parts of LWE secrets
di: Nolte, Niklas, et al.
Pubblicazione: (2024)

Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic
di: Saxena, Eshika, et al.
Pubblicazione: (2024)

Machine learning for modular multiplication
di: Lauter, Kristin, et al.
Pubblicazione: (2024)

An efficient algorithm for integer lattice reduction
di: Charton, François, et al.
Pubblicazione: (2023)

Model Inversion Attacks Meet Cryptographic Fuzzy Extractors
di: Prabhakar, Mallika, et al.
Pubblicazione: (2025)

Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version]
di: Cortellazzi, Jacopo, et al.
Pubblicazione: (2019)

Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks
di: Yao, Likai, et al.
Pubblicazione: (2024)

Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy
di: Saxena, Aman, et al.
Pubblicazione: (2026)

SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models
di: Zhang, Jiang, et al.
Pubblicazione: (2025)

A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
di: elShehaby, Mohamed, et al.
Pubblicazione: (2026)

Attacking Byzantine Robust Aggregation in High Dimensions
di: Choudhary, Sarthak, et al.
Pubblicazione: (2023)

INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression
di: Tokgoz, Gamze Kirman, et al.
Pubblicazione: (2026)

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods
di: Ullah, Mohammad Arafat, et al.
Pubblicazione: (2024)

A General Framework for Data-Use Auditing of ML Models
di: Huang, Zonghao, et al.
Pubblicazione: (2024)

Instance-Level Data-Use Auditing of Visual ML Models
di: Huang, Zonghao, et al.
Pubblicazione: (2025)

Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks
di: Eykholt, Kevin, et al.
Pubblicazione: (2024)

MAIDS: Malicious Agent Identification-based Data Security Model for Cloud Environments
di: Gupta, Kishu, et al.
Pubblicazione: (2024)

Provable Watermarking for Data Poisoning Attacks
di: Zhu, Yifan, et al.
Pubblicazione: (2025)

Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks
di: Liu, Shijie, et al.
Pubblicazione: (2023)

Synthetic Tabular Data: Methods, Attacks and Defenses
di: Cormode, Graham, et al.
Pubblicazione: (2025)

Indiscriminate Data Poisoning Attacks on Neural Networks
di: Lu, Yiwei, et al.
Pubblicazione: (2022)

Attribute Inference Attacks for Federated Regression Tasks
di: Diana, Francesco, et al.
Pubblicazione: (2024)

Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data
di: Simonetto, Thibault, et al.
Pubblicazione: (2024)

SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
di: Paracha, Anum, et al.
Pubblicazione: (2025)

Membership Inference Attacks Beyond Overfitting
di: Khalil, Mona, et al.
Pubblicazione: (2025)

Deep Learning-Assisted Improved Differential Fault Attacks on Lightweight Stream Ciphers
di: Lim, Kok Ping, et al.
Pubblicazione: (2026)

Using Graph Theory for Improving Machine Learning-based Detection of Cyber Attacks
di: Zonneveld, Giacomo, et al.
Pubblicazione: (2024)

Watermarking Needs Input Repetition Masking
di: Khachaturov, David, et al.
Pubblicazione: (2025)

Backdoor Attacks on Transformers for Tabular Data: An Empirical Study
di: Pleiter, Bart, et al.
Pubblicazione: (2023)

Data Reconstruction Attacks and Defenses: A Systematic Evaluation
di: Liu, Sheng, et al.
Pubblicazione: (2024)

Inverting Gradient Attacks Makes Powerful Data Poisoning
di: Bouaziz, Wassim, et al.
Pubblicazione: (2024)

Privacy Amplification Through Synthetic Data: Insights from Linear Regression
di: Pierquin, Clément, et al.
Pubblicazione: (2025)

Stealthy Poisoning Attacks Bypass Defenses in Regression Settings
di: Carnerero-Cano, Javier, et al.
Pubblicazione: (2026)

Attacks on Sparse LWE and Sparse LPN with new Sample-Time tradeoffs
di: Agrawal, Shashwat, et al.
Pubblicazione: (2026)

Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors
di: Lu, Yiwei, et al.
Pubblicazione: (2024)

Decaf: Data Distribution Decompose Attack against Federated Learning
di: Dai, Zhiyang, et al.
Pubblicazione: (2024)

URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning
di: Yao, Duanyi, et al.
Pubblicazione: (2024)