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Found 6 papers, 0 papers with code
Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models
With the rapid development of Large Language Models (LLMs), we have witnessed intense competition among the major LLM products like ChatGPT, LLaMa, and Gemini.
GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models
Graph Neural Networks (GNNs) have increasingly become an indispensable tool in learning from graph-structured data, catering to various applications including social network analysis, recommendation systems, etc.
FLTrojan: Privacy Leakage Attacks against Federated Language Models Through Selective Weight Tampering
Federated learning (FL) is becoming a key component in many technology-based applications including language modeling -- where individual FL participants often have privacy-sensitive text data in their local datasets.
FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks
This highlights the need to design FL systems that are secure and robust against malicious participants' actions while also ensuring high utility, privacy of local data, and efficiency.
Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models
Increasing use of machine learning (ML) technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to better understand if these ML technologies are introducing leakage of sensitive and proprietary training data.
Black-box Model Inversion Attribute Inference Attacks on Classification Models
In this paper, we focus on one kind of model inversion attacks, where the adversary knows non-sensitive attributes about instances in the training data and aims to infer the value of a sensitive attribute unknown to the adversary, using oracle access to the target classification model.
