Archive for Bayesian privacy
off to Tokyo
Posted in Statistics, Travel, University life with tags Bayesian decision theory, Bayesian privacy, Chūbu, conference, Honshu, Institute of Statistical Mathematics, invitation, Japan, Japan Statistical Society, Japanese Alps, JSS 2025 Spring Meeting, Tokyo on March 6, 2025 by xi'an
Houcheanographers!
Posted in pictures, Travel, University life with tags Aiguille du Midi, Bayesian privacy, Chamonix-Mont-Blanc, differential privacy, ERC Synergy Grant, French Alps, group picture, Les Houches, Mont Blanc, Mont Blanc du Tacul, Ocean, Pays du Mont-Blanc, winter light, workshop on February 22, 2025 by xi'an
more oceanographers in Les Houches
Posted in Books, Kids, Mountains, pictures, Running, Statistics, Travel, University life with tags #ERCSyG, Bayesian privacy, Chamonix, CHANCE, differential privacy, ERC, ERC Synergy Grant, fairness, French Alps, Les Houches, Ocean, summer school, uncertainty quantification, workshop on February 2, 2025 by xi'an
Our second internal research workshop on privacy, covered by our ERC Synergy project OCEAN is taking place in Les Houches, French Alps, this very week with 12 researchers gathering for further brain-storming on some of the themes at the core of the project, like algorithmic tools for multiple decision-making agents, along with Bayesian uncertainty quantification and Bayesian learning under constraints (scarcity, fairness, privacy). Due to the small size of the workshop, it will take place in the same local (and uncommon) hotel as last year. And where I had a genuine blackboard delivered (to be donated afterwards to École de Physique des Houches), as the place is not used to mathematicians discussing on and around a blackboard. Even though the schedule does not leave much time for skiing, I hope for a few opportunities, given that the ski cover is quite good this year!
JSM 2024, Portland, miniday 4
Posted in Books, pictures, Running, Statistics, Travel, University life with tags AI, American Statistical Association, ASA, Bayesian decision theory, Bayesian nonparametrics, Bayesian privacy, BIRS, British Columbia, classification, contextual integrity, data science, differential privacy, draw bridge, ERC, federated learning, generalised Bayesian inference, Joint Statistical Meeting, JSM 2024, Kelowna, Ocean, open water swimming, Oregon, Pacific North West, Portland, Seattle, United States of America, University of Washington, Willamette River on August 11, 2024 by xi'an
Final (half)day at JSM is always a sad thing as most people have left, people are busy dismantling booths and packing boxes, and the few remaining participants are fidgety and sitting on their suitcases (there may have been more suitcases than people in the main area that day!), cafés are minimally staffed or simply closed. Hence not the best time-slot to deliver one’s talk! Still, a few dozen people attended our session. The session topic was Bridge the Gap: Differential Privacy and Statistical Analysis, organised by Bei Jiang whom I met last summer in Kelowna, at a BIRS workshop on privacy. Where I spoke on setting up a complete decision-theoretic framework, as developed (and still in development) within our Ocean group, esp. Joshua Bon, Stan du Ché, and Judith Rousseau. (Rather than on the original plan of talking about convergence versus privacy, as a criticism of differential privacy.) The other talks were by Shurong Li, strongly set with differential privacy when record linkage is present, and Xuan Bi on a fully decentralised federated learning with local exchanges of global gradients that limit privacy leaks. (With a mention of gossip learning I hadn’t seen previously!)
I enjoyed even more the session due to Naisyin Wang giving a discussion on the three talks, in closes the particular because I had not seen her in years, if a few times since she was my teaching assistant in Cornell in a Bayesian decision theory class I was building on the spot (with Linda Zhao as a student!). Which nicely closes the loop given the topic of my talk, of which she was quite supportive! While calling for debiasing post-processing and wondering about a two-dimensional decision theoretic perspective rather than a unidimensional one under hard privacy constraints.
On the way out, after parting from the few friends remaining in the convention centre, I spotted the nearby (steel) bridge being raised, although I could not see the boat responsible for it. I had been unaware of this possibility while running over and under it, as well as swimming thrice under it. And we left Portland in the early afternoon, heading for Seattle and a celebration of Adrian Raftery’s career.
differential privacy for Bayesian inference
Posted in Books, pictures, Statistics, Travel, University life with tags ABC algorithm, Bayesian paradigm, Bayesian privacy, differential privacy, JMLR, JSM 2024, Lipschitz continuity, Oregon, Paris, Portland, University of Warwick, USA on July 30, 2024 by xi'an
As I was reading it in preparation for my JSM²⁴ lecture, I found anew that, in this landmark paper of Dimitrikakis et al. (2017), some limitations of the concept of differential privacy were most apparent:
– a requirement to bend both the model and the prior to fit differential privacy, like switching to Lipschitz constraints or using new (e.g., truncated) priors, which runs contrary to Bayesian principles, although the former can be seen as a form of randomization akin to ABC when the randomization itself is accounted for in the derivation of the “exact” posterior distribution (as in the paper of Berah, Favaro, and Rao (2023) on running MCMC for Bayesian non-parametric estimation on privatized (noisy) data I discussed a few days ago);
– a subtle switch of the randomness from the (privatization) procedure itself (as in Dwork (2006)) to the uncertainty about the parameter, not that it clashes per se with Bayesian principles (even though there is an unclear randomness statement in Theorem 9, when the prior itself seems to become random (?)). Which actually means that producing one realisation from the posterior is the (privatization) procedure, as I realised when discussing with Shenggang Hu in Warwick;
– a linear degradation of the privacy parameter ε when moving from one realisation of the posterior to a simulated sample, assuming iid realisations (I wonder whether or not releasing a dependent sample could involve the ESS instead of the number of MCMC iterations). The upper bound means that no privacy whatsoever is guaranteed for an infinite posterior sample, hence for delivering de facto the posterior (despite the paper producing an (ε,0) bound on the Kullback-Leibler measure of the difference between posteriors);
– an absence of prior knowledge or modelling on the data itself, unless the distance from the actual data to an hypothetical alternative, ρ(x,y), can be interpreted as minus a score function conditional on the actual data, eg the opposite of the log predictive
-
the occurrence of an “exponential prior” that is exp p/m ell (theta) with ell a Lipschitz constant for the associated likelihood
-
the assumption that the data user is not an adversary of the data keeper, in that their utility function is about the parameter (and publicly available)
