A large number of studies have focused on these security and privacy problems in reinforcement learning.
We investigate several representative model architectures from CNNs to Transformers, and show that Transformers are generally more vulnerable to privacy attacks than CNNs.
The major challenge is to find a way to guarantee that sensitive personal information is not disclosed while data is published and analyzed.
Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications.
To evaluate the attack efficacy, we crafted heterogeneous security scenarios where the detectors were embedded with different levels of defense and the attackers' background knowledge of data varies.
However, they may not mean that transfer learning models are impervious to model inversion attacks.
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.
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.
In this paper, we propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) method to examine the vulnerability of deep models.
Experimental findings on the testing set show that our scheme preserves image privacy while maintaining the availability of the training set in the deep learning models.
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.
Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server.
Distributed, Parallel, and Cluster Computing
In this way, the impact of data correlation is relieved with the proposed feature selection scheme, and moreover, the privacy issue of data correlation in learning is guaranteed.
A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.
Extensive experiments show that our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.
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.
We devise an efficient mechanism to select host images and watermark images and utilize the improved discrete wavelet transform (DWT) based Patchwork watermarking algorithm with a set of valid hyperparameters to embed digital watermarks from the watermark image dataset into original images for generating image adversarial examples.
Local differential privacy (LDP), as a strong privacy tool, has been widely deployed in the real world in recent years.
Cryptography and Security
Artificial Intelligence (AI) has attracted a great deal of attention in recent years.