English

Condensing and Extracting Against Online Adversaries

Computational Complexity 2025-11-13 v3

Abstract

We study the tasks of deterministically condensing and extracting from Online Non-Oblivious Symbol Fixing (oNOSF) sources, a natural model of defective randomness where extraction is impossible in many parameter regimes [AORSV, EUROCRYPT'20]. A (g,)(g,\ell)-oNOSF source is a sequence of \ell blocks where at least gg blocks are good (independent, with min-entropy) and the remaining bad blocks are controlled by an online adversary and can be arbitrarily correlated with prior blocks. Previously, [CGR, FOCS'24] proved impossibility of condensing beyond rate 1/21/2 when g0.5g\le 0.5 \ell and showed existence of condensers for when g0.51g \ge 0.51\ell and nn is exponential in \ell. In this work, not only do we construct the first explicit condensers matching the existential results of [CGR, FOCS'24], but we make a doubly exponential improvement by handling the case when g0.51g\ge 0.51\ell and nn is only polylogarithmic in \ell. We also obtain a much improved explicit construction for transforming low-entropy oNOSF sources into uniform oNOSF sources. Next, we essentially resolve the question of the existence of condensers for oNOSF sources by showing the existence of condensers even when nn is a large enough constant and \ell is growing (provided g0.51g \ge 0.51\ell). We apply our condensers to collective coin flipping and collective sampling, widely studied problems in fault-tolerant distributed computing, and provide very simple protocols for them. Finally, we study the possibility of extraction from oNOSF sources. For lower bounds, we introduce the notion of online influence - extending the notion of influence of boolean functions - and establish tight bounds that imply extraction lower bounds. We also construct explicit extractors via leader election protocols that beat standard resilient functions [AL, Combinatorica'93].

Cite

@article{arxiv.2411.04115,
  title  = {Condensing and Extracting Against Online Adversaries},
  author = {Eshan Chattopadhyay and Mohit Gurumukhani and Noam Ringach and Rocco Servedio},
  journal= {arXiv preprint arXiv:2411.04115},
  year   = {2025}
}
R2 v1 2026-06-28T19:50:28.208Z