  In a membership inference attack, an attacker aims to infer whether a data
sample is in a target classifier's training dataset or not. Specifically, given
a black-box access to the target classifier, the attacker trains a binary
classifier, which takes a data sample's confidence score vector predicted by
the target classifier as an input and predicts the data sample to be a member
or non-member of the target classifier's training dataset. Membership inference
attacks pose severe privacy and security threats to the training dataset. Most
existing defenses leverage differential privacy when training the target
classifier or regularize the training process of the target classifier. These
defenses suffer from two key limitations: 1) they do not have formal
utility-loss guarantees of the confidence score vectors, and 2) they achieve
suboptimal privacy-utility tradeoffs.
  In this work, we propose MemGuard, the first defense with formal utility-loss
guarantees against black-box membership inference attacks. Instead of tampering
the training process of the target classifier, MemGuard adds noise to each
confidence score vector predicted by the target classifier. Our key observation
is that attacker uses a classifier to predict member or non-member and
classifier is vulnerable to adversarial examples. Based on the observation, we
propose to add a carefully crafted noise vector to a confidence score vector to
turn it into an adversarial example that misleads the attacker's classifier.
Our experimental results on three datasets show that MemGuard can effectively
defend against membership inference attacks and achieve better privacy-utility
tradeoffs than existing defenses. Our work is the first one to show that
adversarial examples can be used as defensive mechanisms to defend against
membership inference attacks.
