Inference Attacks: Understanding Privacy in the Era of &#34Privacy is Dead&#34

As data from far-reaching sources is collected, aggregated, and re-packaged to
enable new and smarter applications, confidentiality and data security are at
greater risk than ever before. Some of the most surprising and invasive
threats to materialize in recent years are brought about by so-called
inference attacks: successful attempts to learn sensitive information by
leveraging public data such as social network updates, published research
articles, and web APIs.

In this talk, I will focus on two of my research efforts to better understand
and defend against these attacks. First I will discuss work that examines the
privacy risks that arise when machine learning models are used in a popular
medical application, and illustrate the consequences of applying differential
privacy as a defense. This work uncovered a new type of inference attack on
machine learning models, and shows via an in-depth case study how to
understand privacy "in situ" by balancing the attacker's chance of success
against the likelihood of harmful medical outcomes. The second part of the
talk will detail work that helps developers correctly write privacy-aware
applications using verification tools. I will illustrate how a wide range of
confidentiality guarantees can be framed in terms of a new logical primitive
called Satisfiability Modulo Counting, and describe a tool that I have
developed around this primitive that automatically finds privacy bugs in
software (or proves that the software is bug-free). Through a better
understanding of how proposed defenses impact real applications, and by
providing tools that help developers implement the correct defense for their
task, we can begin to proactively identify potential threats to privacy and
take steps to ensure that they will not surface in practice.
Matt Fredrikson is a Ph.D. candidate in the department of Computer Sciences at
the University of Wisconsin-Madison. His research interests lie at the
intersection of security, privacy, and formal methods, covering topics in
software security, privacy issues associated with machine-learning models, and
applied cryptography. His work has been profiled by Reuters, Technology
Review, and New Scientist, and received the best paper award at USENIX
Security 2014. He is a recipient of the Microsoft Research Graduate Fellowship
Award.