With an increase in low-cost machine learning APIs, advanced machine learning models
may be trained on private datasets and monetized by providing them as a service. However …

The success of natural language processing (NLP) is making NLP applications
commonplace. Unfortunately, recent research has shown that privacy might be at stake …

Purpose Recently, deep learning (DL) has been widely applied in various aspects of human
endeavors. However, studies have shown that DL models may also be a primary cause of …

Membership inference is one of the simplest privacy threats faced by machine learning
models that are trained on private sensitive data. In this attack, an adversary infers whether a …

Membership Inference Attack (MIA) identifies whether a record exists in a machine learning
model's training set by querying the model. MIAs on the classic classification models have …

Machine learning models are commonly trained on sensitive and personal data such as
pictures, medical records, financial records, etc. A serious breach of the privacy of this …

In recent years, machine learning (ML) has achieved huge success in healthcare and
medicine areas. However, recent work has demonstrated that ML is vulnerable to privacy …

Deep learning models, while achieving remarkable performance across various tasks, are
vulnerable to member inference attacks, wherein adversaries identify if a specific data point …

Recent researches demonstrate that deep learning models are vulnerable to membership
inference attacks. Few defenses have been proposed, but suffer from compromising the …

Machine Learning (ML) has made unprecedented progress in the past several decades.
However, due to the memorability of the training data, ML is susceptible to various attacks …