I am quite excited about the paper James Baillie, Joshua Bon, Judith Rousseau, and myself just arXived! A novel framework for measuring privacy we have been working on for at least the past year, partly through the previous Les Houches privacy workshops. In the spirit of these workshops and the larger scale ERC Synergy grant OCEAN, we develop therein a rather generic Bayesian game-theoretic perspective on achieving statistical privacy. It involves a Sender (observing the original data and delivering a limited output) and a Receiver (with potential adversarial intentions). The paper mostly focus on setting a theoretical framework, including the creation of new, purpose-driven privacy definitions that are rigorously justified, while also allowing for the assessment of existing privacy guarantees through game theory. While this was not our original intent, we show that pure and probabilistic differential privacy notions, in the Dwork et al. (2006) sense, are special cases of our framework. This setting provides new interpretations of the post-processing inequality. Furthermore, and somewhat more importantly, we also prove that our privacy guarantees can be established for deterministic algorithms, which are outside current privacy standards. Hopefully, we’ll make further progress at the incoming privacy workshop next month, to be held in Venice (again).
Archive for Bayesian privacy
persuasive (and Oceanic) privacy
Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags #ERCSyG, adversarial strategy, arXiv, Bayesian decision theory, Bayesian privacy, decision-making agents, differential privacy, ERC, ERC Synergy Grant, fairness, French Alps, game theory, Les Houches, Ocean, uncertainty quantification, Università Ca' Foscari Venezia, Université Paris Dauphine, Venice, workshop on February 3, 2026 by xi'anISBA 2026 (Nagoya, Japan, 28 June – 03 July)
Posted in Mountains, pictures, Statistics, Travel, University life with tags Bayesian privacy, call for contributions, ISBA, Japan, Kyushu, logo, Nagoya, Oceanerc, privacy, Shachihoko, Shinkansen on October 30, 2025 by xi'an
meanwhile, today, in Nashville
Posted in Books, pictures, Statistics, Travel, University life with tags #ERCSyG, American Statistical Association, Bayesian privacy, differential privacy, ERCIM, John Tukey, JSM 2025, Nashville, Ocean, privacy, Pufferfish flavor, Tennessee, Trump administration, U.S. Customs and Border Protection, USA on August 5, 2025 by xi'an
[Josh and I organised this privacy session a while ago, before Josh returned to Adelaïde, and we found neither of us could/would attend JSM!]
Data Privacy: Frontiers and Barriers of Differential Privacy
Main Sponsor
Presentations
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
Speaker: Alireza Fallah, UC Berkeley
Composition of privacy mechanisms: Only fresh noise counts
Speaker: James Bailie, Harvard University
Tukey Depth Mechanisms for Practical Private Mean Estimation
In this talk, I will discuss first steps to bridge this gap by implementing the (Restricted) Tukey Depth Mechanism, a theoretically optimal mean estimator for multivariate Gaussian distributions, yielding improved practical methods for private mean estimation. The implementations enable the use of these mechanisms for small sample sizes or low-dimensional data. Additionally, I will present variants of these mechanisms that use approximate versions of Tukey depth, trading off accuracy for faster computation. We demonstrate their efficiency in practice, showing that they are viable options for modest dimensions. Given their strong accuracy and robustness guarantees, we contend that they are competitive approaches for mean estimation in this regime. Finally, I will discuss future directions for improving the computational efficiency of these algorithms by leveraging fast polytope volume approximation techniques, paving the way for more accurate private mean estimation in higher dimensions, as well as conjectured barriers toward this goal.
This talk is based on joint work with Gavin Brown.
Speaker: Lydia Zakynthinou, UC Berkeley
Bayesian decision-theory for data privacy [surfin’ the Oce’n, 30 April, INRIA Paris]
Posted in Statistics, University life with tags #ERCSyG, Bayesian decision theory, Bayesian privacy, decision theory, differential privacy, ERC, ERC Synergy Grant, European Research Council, hybrid Monte Carlo, INRIA, INRIA Paris, MCMC, Monte Carlo methods, Ocean, Paris, privacy, seminar, Telecom Paris on April 23, 2025 by xi'an
Abstract
The scientific and economic value of data continues to grow alongside technology advances. New hardware and software developments enable, but often require, larger and more complex datasets to function effectively. As the importance of input data to these systems becomes increasingly recognized, so too does the loss of privacy for data providers. In this context, data privacy emerges as a critical issue for fields such as statistics and machine learning, as well as for scientific and industrial endeavours that rely on sensitive data. We propose a framework for measuring privacy from a Bayesian decision-theoretic perspective. This framework enables the creation of new, purpose-driven privacy principles that are rigorously justified, while also allowing for the assessment of existing privacy definitions through decision theory. We pay particular attention to the privacy of deterministic algorithms, which are overlooked by current privacy standards, and to the privacy of N Monte Carlo samples drawn from an invariant distribution as N goes to infinity. We show that Probabilistic Differential Privacy is a special case of our framework and provide some new interpretations for Differential Privacy as a result.
handbook of sharing confidential data [book review]
Posted in Statistics with tags Bayesian privacy, book review, Census Bureau, CHANCE, Chapman & Hall, CRC Press, deep learning, differential privacy, EU, GDPR, handbook, LaTeX, machine learning, PRNG on March 12, 2025 by xi'an
A new Chapman & Hall handbook appeared on the most current issue of confidentiality and privacy, which has been edited by Jörg Drechsler, Daniel Kifer, Jerome Reiter, and Aleksandra Slavković. The forty authors of the 18 chapters are mostly from the U.S., with a few outliers from Edinburgh (involved in two chapters on protecting the Scottish Longitudinal Study and the U.S. IRS tax data) and Tallinn (for a chapter on secure multi-party computation applications). This means a more U.S. centric focus for realistic implementations as, e.g., with the Census Bureau (which employs 25% of the authors), than those implied by EU regulations, for instance.
Overall, I enjoyed reading these chapters and would certainly use the book as a first entry to a graduate course on privacy (as opposed to some books I recently reviewed). The first two chapters are 100% formula-free and thus more surveys than informative entries to the field, imho. The following Part II on formal privacy techniques covers the expected standards of differential privacy, local vs. global design, single vs. multiple queries, consequence on learning machines and statistical procedures. Concerning Bayesian aspects, Chapter 7 about private machine learning has two paragraphs on the privacy properties of MCMC algorithms albeit not exposing clearly enough that privacy vanishes as the number of iterations grows to infinity. Chapter 8 concentrates on statistical differential privacy, much along my own perception of the requirements for a genuine statistical approach, with Bayesian aspects not sidelined. If less critical of differential privacy than I. Chapter 9 focusses on system issues, investing a dozen pages into the specifics of pseudo-random generators. Part III is about synthetic data, with some overlap between the first two chapters. (I would deem DP need not be introduced by Chapter 12.) I find the section rather superficial, mostly formula free, and lacking in the statistical impact.
As an aside, I am disappointed at the poor rendering of (mathematical) equations making me wonder which type of LaTeX, if any, was used. There are even genuine typos that seem to result from cut and past encoding errors (see, e.g., the final accentuated c of Sklavković). The reference lists are plentiful, see e.g. the 164 entries for Chapter 7, to the point it would have made more sense to regroup them into a single bibliography. (The predictable reply being that chapters are sold separately and need their respective reference lists.)
[Disclaimer about potential self-plagiarism: this post or an edited version of it could possibly appear in my Books Review section in CHANCE.]
