A Cryptographic Perspective on Mitigation vs. Detection in Machine Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Gluch, Greg, Goldwasser, Shafi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment
by: Ball, Sarah, et al.
Published: (2025)
by: Ball, Sarah, et al.
Published: (2025)
Planting Undetectable Backdoors in Machine Learning Models
by: Goldwasser, Shafi, et al.
Published: (2022)
by: Goldwasser, Shafi, et al.
Published: (2022)
SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacy
by: Capano, Francesco, et al.
Published: (2026)
by: Capano, Francesco, et al.
Published: (2026)
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective
by: Li, Nan, et al.
Published: (2024)
by: Li, Nan, et al.
Published: (2024)
Machine Learning Transferability for Malware Detection
by: Vieira, César, et al.
Published: (2026)
by: Vieira, César, et al.
Published: (2026)
Feature Selection via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation
by: Patel, Harsh
Published: (2025)
by: Patel, Harsh
Published: (2025)
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach
by: Kumar, Mohit, et al.
Published: (2023)
by: Kumar, Mohit, et al.
Published: (2023)
Oblivious Defense in ML Models: Backdoor Removal without Detection
by: Goldwasser, Shafi, et al.
Published: (2024)
by: Goldwasser, Shafi, et al.
Published: (2024)
A Study on the Importance of Features in Detecting Advanced Persistent Threats Using Machine Learning
by: Hallaji, Ehsan, et al.
Published: (2025)
by: Hallaji, Ehsan, et al.
Published: (2025)
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning
by: Wang, Liyang, et al.
Published: (2024)
by: Wang, Liyang, et al.
Published: (2024)
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
by: Tripathy, Sudhanshu Sekhar, et al.
Published: (2025)
by: Tripathy, Sudhanshu Sekhar, et al.
Published: (2025)
An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey
by: Ige, Tosin, et al.
Published: (2024)
by: Ige, Tosin, et al.
Published: (2024)
A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation
by: Jing, Phoebe, et al.
Published: (2024)
by: Jing, Phoebe, et al.
Published: (2024)
Android Malware Detection: A Machine Leaning Approach
by: Abdulla, Hasan
Published: (2025)
by: Abdulla, Hasan
Published: (2025)
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
by: Xu, Zhaoyang, et al.
Published: (2025)
by: Xu, Zhaoyang, et al.
Published: (2025)
NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques
by: Thaqi, Leand, et al.
Published: (2024)
by: Thaqi, Leand, et al.
Published: (2024)
Automated Creation of Source Code Variants of a Cryptographic Hash Function Implementation Using Generative Pre-Trained Transformer Models
by: Pelofske, Elijah, et al.
Published: (2024)
by: Pelofske, Elijah, et al.
Published: (2024)
Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models
by: Thapa, Jikesh, et al.
Published: (2025)
by: Thapa, Jikesh, et al.
Published: (2025)
ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks
by: Ren, Zhiyao, et al.
Published: (2025)
by: Ren, Zhiyao, et al.
Published: (2025)
A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data
by: Lotfi, Zahra, et al.
Published: (2025)
by: Lotfi, Zahra, et al.
Published: (2025)
Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems
by: Lima, Mateus Guimarães, et al.
Published: (2024)
by: Lima, Mateus Guimarães, et al.
Published: (2024)
Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space
by: Vyas, Sanyam, et al.
Published: (2024)
by: Vyas, Sanyam, et al.
Published: (2024)
Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention
by: Rabanser, Stephan, et al.
Published: (2025)
by: Rabanser, Stephan, et al.
Published: (2025)
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries
by: Çolhak, Furkan, et al.
Published: (2025)
by: Çolhak, Furkan, et al.
Published: (2025)
Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
by: Ahamed, Sayyed Farid, et al.
Published: (2024)
by: Ahamed, Sayyed Farid, et al.
Published: (2024)
GPML: Graph Processing for Machine Learning
by: Jaber, Majed, et al.
Published: (2025)
by: Jaber, Majed, et al.
Published: (2025)
The Data Minimization Principle in Machine Learning
by: Ganesh, Prakhar, et al.
Published: (2024)
by: Ganesh, Prakhar, et al.
Published: (2024)
Adversarial Machine Learning Threats to Spacecraft
by: Thummala, Rajiv, et al.
Published: (2024)
by: Thummala, Rajiv, et al.
Published: (2024)
Locking Machine Learning Models into Hardware
by: Clifford, Eleanor, et al.
Published: (2024)
by: Clifford, Eleanor, et al.
Published: (2024)
Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions
by: Sai, Siva, et al.
Published: (2025)
by: Sai, Siva, et al.
Published: (2025)
RMF: A Risk Measurement Framework for Machine Learning Models
by: Schröder, Jan, et al.
Published: (2024)
by: Schröder, Jan, et al.
Published: (2024)
A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning
by: Peng, Zhizhi, et al.
Published: (2025)
by: Peng, Zhizhi, et al.
Published: (2025)
Machine Learning-Based Security Policy Analysis
by: Jain, Krish, et al.
Published: (2024)
by: Jain, Krish, et al.
Published: (2024)
Bridging Privacy and Robustness for Trustworthy Machine Learning
by: Zhang, Xiaojin, et al.
Published: (2024)
by: Zhang, Xiaojin, et al.
Published: (2024)
Mitigating Many-Shot Jailbreaking
by: Ackerman, Christopher M., et al.
Published: (2025)
by: Ackerman, Christopher M., et al.
Published: (2025)
Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation
by: Jiang, Yukun, et al.
Published: (2024)
by: Jiang, Yukun, et al.
Published: (2024)
DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective
by: Shao, Shuo, et al.
Published: (2025)
by: Shao, Shuo, et al.
Published: (2025)
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
by: Gomez, Juan Felipe, et al.
Published: (2025)
by: Gomez, Juan Felipe, et al.
Published: (2025)
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
by: Li, Zhuohang, et al.
Published: (2024)
by: Li, Zhuohang, et al.
Published: (2024)
Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning
by: Chandrinos, Nikolaos, et al.
Published: (2024)
by: Chandrinos, Nikolaos, et al.
Published: (2024)
Similar Items
-
On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment
by: Ball, Sarah, et al.
Published: (2025) -
Planting Undetectable Backdoors in Machine Learning Models
by: Goldwasser, Shafi, et al.
Published: (2022) -
SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacy
by: Capano, Francesco, et al.
Published: (2026) -
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective
by: Li, Nan, et al.
Published: (2024) -
Machine Learning Transferability for Malware Detection
by: Vieira, César, et al.
Published: (2026)