The past decade has witnessed the widespread adoption of machine learning and statistical methods on large-scale datasets, many of which correspond to personal data of individuals. While this has enabled unprecedented insights into human behaviour, at the same time, it raises new moral and ethical concerns about what might be revealed as a byproduct of these analyses. What private information will this allow us to infer about individuals, and is this worth the price of admission? Are there strategies which we can adopt to avoid these disclosures, and can they be executed without significant loss in utility? At which point should lawmakers step in, and how do we connect technical notions of privacy with those which are enforced by law? Beyond individual privacy, should there be regulations on things that "shouldn't be learned"? The goal of this social is to bring together a broad range of experts and non-experts interested in all aspects of data privacy, discuss associated issues and challenges, and propose and debate potential solutions. We will lead participants through an exploration of these concepts, featuring fun and interactive activities, as well as a guided discussion on these topics.