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Found 9 papers, 0 papers with code
Reinforcement Unlearning
Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners.
Boosting Model Inversion Attacks with Adversarial Examples
Hence, in this paper, we propose a new training paradigm for a learning-based model inversion attack that can achieve higher attack accuracy in a black-box setting.
New Challenges in Reinforcement Learning: A Survey of Security and Privacy
A large number of studies have focused on these security and privacy problems in reinforcement learning.
One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy
The experimental results show the method to be an effective and timely defense against both membership inference and model inversion attacks with no reduction in accuracy.
Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It
However, they may not mean that transfer learning models are impervious to model inversion attacks.
Label-only Model Inversion Attack: The Attack that Requires the Least Information
In launching a contemporary model inversion attack, the strategies discussed are generally based on either predicted confidence score vectors, i. e., black-box attacks, or the parameters of a target model, i. e., white-box attacks.
DP-Image: Differential Privacy for Image Data in Feature Space
The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public.
Differentially Private Multi-Agent Planning for Logistic-like Problems
To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems.
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Artificial Intelligence (AI) has attracted a great deal of attention in recent years.
