In response to calls for open data and growing privacy threats, organizations are increasingly adopting privacy-preserving techniques that add noise to published datasets. These techniques seek to protect privacy of data subjects while enabling useful analyses. With expert feedback, we developed empirically-driven documentation explaining the noise characteristics of two Wikipedia pageview datasets: one using rounding (heuristic privacy) and another using differential privacy (DP, formal privacy). We then used these documents to conduct a task-based contextual inquiry (n=15) exploring how data users—largely unfamiliar with these methods—perceive, interact with, and interpret privacy-preserving noise during data analysis.

CS departments in the USA have used various models for teaching about ethics, including standalone ethics courses and expert-designed assignments. At Brown University, we are trying a different approach: a group of undergraduate teaching assistants dedicated to socially-responsible practices in computing work with faculty to integrate content into multiple assignments both across a course and across the curriculum. This "responsible computing" initiative has resulted in a variety of assignments added to 13 different courses in the past year. This paper describes the program's design, sample assignments, results of an internal evaluation, and refinements we are making to the model. We contrast our model to others from the literature in hopes of expanding the collection of curricular ideas for designing education in responsible computing.