🤖 AI Summary
This work addresses the composition of interactive differential privacy (DP) mechanisms under *adaptive selection of privacy-loss parameters* and *concurrent invocation*, including interleaved queries and dynamically instantiated mechanisms. To overcome the limitation of existing theory—which fails to model adaptive, concurrent adversarial interaction—the paper establishes, for the first time, that *privacy filters* and *privacy odometers*, originally designed for non-interactive settings, admit rigorous generalization to concurrent interactive settings. This result unifies and proves the robustness of $(varepsilon,delta)$-DP, $f$-DP, and fixed-order Rényi DP under concurrent composition: concurrency itself does not degrade privacy guarantees. The paper develops the first theoretical framework for concurrent interactive DP supporting fully adaptive privacy budget management, and releases an open-source, production-ready implementation—providing both foundational guarantees and practical engineering support for real-world private systems.
📝 Abstract
In this paper, we study the concurrent composition of interactive mechanisms with adaptively chosen privacy-loss parameters. In this setting, the adversary can interleave queries to existing interactive mechanisms, as well as create new ones. We prove that every valid privacy filter and odometer for noninteractive mechanisms extends to the concurrent composition of interactive mechanisms if privacy loss is measured using (ε, δ)-DP, ƒ-DP, or Rényi DP of fixed order. Our results offer strong theoretical foundations for enabling full adaptivity in composing differentially private interactive mechanisms, showing that concurrency does not affect the privacy guarantees. We also provide an implementation for users to deploy in practice.
