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 decision-making agents
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'anOcean’s four!
Posted in Books, pictures, Statistics, University life with tags #ERCSyG, Bayesian Analysis, Brussels, data privacy, data processing, decentralized networks, decision-making agents, ERC, European Research Council, machine learning, multi-agent decision theory, murmuration, Ocean, probabilistic machine learning, starlings, statistical inference, stochastic optimization, The Ocean at the End of the Lane, uncertainty quantification, whales on October 25, 2022 by xi'an
Fantastic news! The ERC-Synergy¹ proposal we submitted last year with Michael Jordan, Éric Moulines, and Gareth Roberts has been selected by the ERC (which explains for the trips to Brussels last month). Its acronym is OCEAN [hence the whale pictured by a murmuration of starlings!], which stands for On intelligenCE And Networks: Mathematical and Algorithmic Foundations for Multi-Agent Decision-Making. Here is the abstract, which will presumably turn public today along with the official announcement from the ERC:
Until recently, most of the major advances in machine learning and decision making have focused on a centralized paradigm in which data are aggregated at a central location to train models and/or decide on actions. This paradigm faces serious flaws in many real-world cases. In particular, centralized learning risks exposing user privacy, makes inefficient use of communication resources, creates data processing bottlenecks, and may lead to concentration of economic and political power. It thus appears most timely to develop the theory and practice of a new form of machine learning that targets heterogeneous, massively decentralized networks, involving self-interested agents who expect to receive value (or rewards, incentive) for their participation in data exchanges.
OCEAN will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making agents, including uncertainty quantification at the agent’s level. OCEAN will study the interaction of learning with market constraints (scarcity, fairness), connecting adaptive microeconomics and market-aware machine learning.
OCEAN builds on a decade of joint advances in stochastic optimization, probabilistic machine learning, statistical inference, Bayesian assessment of uncertainty, computation, game theory, and information science, with PIs having complementary and internationally recognized skills in these domains. OCEAN will shed a new light on the value and handling data in a competitive, potentially antagonistic, multi-agent environment, and develop new theories and methods to address these pressing challenges. OCEAN requires a fundamental departure from standard approaches and leads to major scientific interdisciplinary endeavors that will transform statistical learning in the long term while opening up exciting and novel areas of research.
Since the ERC support in this grant mostly goes to PhD and postdoctoral positions, watch out for calls in the coming months or contact us at any time.
