Mostly short talks. First talk by Thomas Seinke (Google) on interpreting ε, with a side wondering of mine on the relation between exp(ε) and the uncertainty that comes with Monte Carlo outcome. Which may relate to this 2022 paper by Ruobin Gong. Second talk by Gautam Kamath (U Waterloo) on large language models under privacy with “public” data. Questioning the appropriateness of ML benchmarks in terms of privacy. Third talk by Mark Bun (Boston U) on replicability, privacy and adaptive generalisation in machine learning, with a strange criticism of confidence intervals on the same parameter not intersecting for two independent studies. And proposing high probability replicable algorithms that can be put in duality with differentially private algorithms at the cost of lowering precision and effective sample size. We also had another group discussion on how to reach out about privacy guarantees, which made me realise there were GDPR compliance software available.
In the afternoon session, Shlomi Hod (Boston U) presented a practical case of designing a privacy preserving protocol for the Israeli birth record. With a strong opposition from stakeholders to use synthetic data, due to a semantic drift from synthetic to manipulated to fake, to lying. Wanrong Zhang did not talk about her stunning recent ICML paper but instead of another practical case connected with mobile based Covid case predictions, by adding minimal noise to mobility data. Nidhi Hegde (U Alberta) gave up talking on Thomson sampling with privacy protection, to focus on an ongoing health application for Alberta as more suited for the workshop. And Ria Safavi-Naini (U Calgary) drew a parallel between information theory and DP versus CI.
While the workshop was scheduled till Friday noon, in usual BIRS habits (!), the morning session was cancelled for most people leaving Kelowna in the morning.
Xi'an's Og 