However, the DL models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the training dataset.
Neural networks are susceptible to data inference attacks such as the model inversion attack and the membership inference attack, where the attacker could infer the reconstruction and the membership of a data sample from the confidence scores predicted by the target classifier.
More importantly, we show that our attack in multiple cases outperforms the classical membership inference attack on the original ML model, which indicates that machine unlearning can have counterproductive effects on privacy.
Given that GAN could effectively learn the distribution of training data, GAN-based attacks aim to reconstruct human-distinguishable images from victim's personal dataset.
Our privacy risk score metric measures an individual sample's likelihood of being a training member, which allows an adversary to perform membership inference attacks with high confidence.
We investigate the impact of both the data and ML model properties on the vulnerability of ML techniques to MIA.
This problem severely impacts the clustering quality and the efficiency of a differentially private algorithm.
With the prevalence of machine learning services, crowdsourced data containing sensitive information poses substantial privacy challenges.
We empirically compare local and central differential privacy mechanisms under white- and black-box membership inference to evaluate their relative privacy-accuracy trade-offs.
Second, through MPLens, we highlight how the vulnerability of pre-trained models under membership inference attack is not uniform across all classes, particularly when the training data itself is skewed.