  As machine learning (ML) becomes more and more powerful and easily
accessible, attackers increasingly leverage ML to perform automated large-scale
inference attacks in various domains. In such an ML-equipped inference attack,
an attacker has access to some data (called public data) of an individual, a
software, or a system; and the attacker uses an ML classifier to automatically
infer their private data. Inference attacks pose severe privacy and security
threats to individuals and systems. Inference attacks are successful because
private data are statistically correlated with public data, and ML classifiers
can capture such statistical correlations. In this chapter, we discuss the
opportunities and challenges of defending against ML-equipped inference attacks
via adversarial examples. Our key observation is that attackers rely on ML
classifiers in inference attacks. The adversarial machine learning community
has demonstrated that ML classifiers have various vulnerabilities. Therefore,
we can turn the vulnerabilities of ML into defenses against inference attacks.
For example, ML classifiers are vulnerable to adversarial examples, which add
carefully crafted noise to normal examples such that an ML classifier makes
predictions for the examples as we desire. To defend against inference attacks,
we can add carefully crafted noise into the public data to turn them into
adversarial examples, such that attackers' classifiers make incorrect
predictions for the private data. However, existing methods to construct
adversarial examples are insufficient because they did not consider the unique
challenges and requirements for the crafted noise at defending against
inference attacks. In this chapter, we take defending against inference attacks
in online social networks as an example to illustrate the opportunities and
challenges.
