STOC 2020 was recently held online, as one of the first major theory conferences during the COVID-19 era. It featured four papers on differential privacy, which we list and link below. Each one is accompanied by a video from the conference, as well as a longer video if available. Please let us know if we missed any papers on differential privacy, either in the comments below or by email.

• The Power of Factorization Mechanisms in Local and Central Differential Privacy (video)
  Alexander Edmonds, Aleksandar Nikolov, Jonathan Ullman

• Private Stochastic Convex Optimization: Optimal Rates in Linear Time (video)
  Vitaly Feldman, Tomer Koren, Kunal Talwar

• Interaction is necessary for distributed learning with privacy or communication constraints (video)
  Yuval Dagan, Vitaly Feldman

• Does Learning Require Memorization? A Short Tale about a Long Tail (video, longer video)
  Vitaly Feldman