1 Search Results for "Jawale, Ruta"

Locally Covert Learning

Authors: Justin Holmgren and Ruta Jawale

Published in: LIPIcs, Volume 267, 4th Conference on Information-Theoretic Cryptography (ITC 2023)

The goal of a covert learning algorithm is to learn a function f by querying it, while ensuring that an adversary, who sees all queries and their responses, is unable to (efficiently) learn any more about f than they could learn from random input-output pairs. We focus on a relaxation that we call local covertness, in which queries are distributed across k servers and we only limit what is learnable by k - 1 colluding servers. For any constant k, we give a locally covert algorithm for efficiently learning any Fourier-sparse function (technically, our notion of learning is improper, agnostic, and with respect to the uniform distribution). Our result holds unconditionally and for computationally unbounded adversaries. Prior to our work, such an algorithm was known only for the special case of O(log n)-juntas, and only with k = 2 servers [Yuval Ishai et al., 2019]. Our main technical observation is that the original Goldreich-Levin algorithm only utilizes i.i.d. pairs of correlated queries, where each half of every pair is uniformly random. We give a simple generalization of this algorithm in which pairs are replaced by k-tuples in which any k - 1 components are jointly uniform. The cost of this generalization is that the number of queries needed grows exponentially with k.

Cite as

Justin Holmgren and Ruta Jawale. Locally Covert Learning. In 4th Conference on Information-Theoretic Cryptography (ITC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 267, pp. 14:1-14:12, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

  author =	{Holmgren, Justin and Jawale, Ruta},
  title =	{{Locally Covert Learning}},
  booktitle =	{4th Conference on Information-Theoretic Cryptography (ITC 2023)},
  pages =	{14:1--14:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-271-6},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{267},
  editor =	{Chung, Kai-Min},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITC.2023.14},
  URN =		{urn:nbn:de:0030-drops-183421},
  doi =		{10.4230/LIPIcs.ITC.2023.14},
  annote =	{Keywords: learning theory, adversarial machine learning, zero knowledge, Fourier analysis of boolean functions, Goldreich-Levin algorithm, Kushilevitz-Mansour algorithm}
  • Refine by Author
  • 1 Holmgren, Justin
  • 1 Jawale, Ruta

  • Refine by Classification
  • 1 Security and privacy → Information-theoretic techniques
  • 1 Theory of computation → Machine learning theory

  • Refine by Keyword
  • 1 Fourier analysis of boolean functions
  • 1 Goldreich-Levin algorithm
  • 1 Kushilevitz-Mansour algorithm
  • 1 adversarial machine learning
  • 1 learning theory
  • Show More...

  • Refine by Type
  • 1 document

  • Refine by Publication Year
  • 1 2023

Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail