International Association for Cryptologic Research

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Paper: Secure Sampling with Sublinear Communication

Authors:
Seung Geol Choi , United States Naval Academy
Dana Dachman-Soled , University of Maryland
S. Dov Gordon , George Mason University
Linsheng Liu , George Washington University
Arkady Yerukhimovich , George Washington University
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Presentation: Slides
Conference: TCC 2022
Abstract: Random sampling from specified distributions is an important tool with wide applications for analysis of large-scale data. In this paper we study how to randomly sample when the distribution is partitioned among two parties' private inputs. Of course, a trivial solution is to have one party send a (possibly encrypted) description of its weights to the other party who can then sample over the entire distribution (possibly using homomorphic encryption). However, this approach requires communication that is linear in the input size which is prohibitively expensive in many settings. In this paper, we investigate secure 2-party sampling with sublinear communication for many standard distributions. We develop protocols for L_1, and L_2 sampling. Additionally, we investigate the feasibility of sublinear product sampling, showing impossibility for the general problem and showing a protocol for a restricted case of the problem. We additionally show how such product sampling can be used to instantiate a sublinear communication 2-party exponential mechanism for differentially-private data release.
BibTeX
@inproceedings{tcc-2022-32570,
  title={Secure Sampling with Sublinear Communication},
  publisher={Springer-Verlag},
  author={Seung Geol Choi and Dana Dachman-Soled and S. Dov Gordon and Linsheng Liu and Arkady Yerukhimovich},
  year=2022
}