This talk will be an introduction to the study of privacy attacks in the context of machine learning, aimed at those unfamiliar with the literature. We will discuss who the stakeholders are, what information may be attacked, how it may be attacked, and why. The “how” will be at a high level, illustrated through some specific examples of privacy attacks. Much of the material will be from a recent survey of privacy attacks by Maria Rigaki and Sebastian Garcia at Czech Technical University in Prague, although their threat model will be extended slightly to consider cases that include synthetic data. The goal of the talk is to give the audience an appreciation of some of the complications of privacy preservation (i.e. that it’s not as simple as it may be assumed to be) and familiarity with some of the terminology.
Joe Pedersen is from Pittsburgh, PA. He earned a B.S. in mathematics from Penn State in 2006, after which he was commissioned into the U.S. Army as an Infantry officer. After being selected by the U.S. Army to attend graduate school, he earned master’s degrees in math and physics from RPI in 2013, followed by a three-year teaching position in the Department of Mathematical Sciences at the United States Military Academy. From 2016 to 2019, he served as an operations research analyst at Fort Benning, GA. He is in his second year of a PhD program in the Department of Industrial and Systems Engineering at RPI, after which he will return to the United States Military Academy as an assistant professor in the Department of Systems Engineering. He is focusing his studies in the areas of machine learning and data analytics.