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Found 7 papers, 3 papers with code
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
The integration of machine learning (ML) in numerous critical applications introduces a range of privacy concerns for individuals who provide their datasets for model training.
Privacy Side Channels in Machine Learning Systems
Most current approaches for protecting privacy in machine learning (ML) assume that models exist in a vacuum, when in reality, ML models are part of larger systems that include components for training data filtering, output monitoring, and more.
Poisoning Network Flow Classifiers
As machine learning (ML) classifiers increasingly oversee the automated monitoring of network traffic, studying their resilience against adversarial attacks becomes critical.
Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT's Potential to Apply Graph Layout Algorithms
Large language models (LLMs) have recently taken the world by storm.
Network-Level Adversaries in Federated Learning
Federated learning is a popular strategy for training models on distributed, sensitive data, while preserving data privacy.
Subpopulation Data Poisoning Attacks
Poisoning attacks against machine learning induce adversarial modification of data used by a machine learning algorithm to selectively change its output when it is deployed.
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers
Training pipelines for machine learning (ML) based malware classification often rely on crowdsourced threat feeds, exposing a natural attack injection point.
