2026-09-18T22:33:19Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1228712021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Vinod Vaikuntanathan.
Hui, Joseph,S.M.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-12T17:40:34Z
2019-11-12T17:40:34Z
2018
2018
https://hdl.handle.net/1721.1/122871
1126650513
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 39-40).
We study the problem of distributed correlation generators wherein n parties wish to simulate unbounded samples from a joint distribution D = Di x D2 X ... x D[subscript n], once they are initialized using randomness sampled from a (possibly different) correlated distribution. We wish to ensure that these samples are computationally indistinguishable from i.i.d. samples from D. Furthermore, we wish to ensure security even against an adversary who corrupts a subset of the parties and obtains their internal (initialization) state. Our contributions are three-fold. First, we define the notion of distributed (noninteractive) correlation generators and show its connection to other cryptographic primitives. Secondly, assuming the existence of indistinguishability obfuscators, we show a construction of distributed correlation generators for a large and natural class of joint distributions that we call conditionally sampleable distributions. Finally, we show a construction for the subclass of additive-spooky distributions assuming private constrained pseudorandom functions (private CPRFs).
by Joseph Hui.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
40 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Distributed correlation generators
Thesis
application/pdf
2019-11-12T17:40:33Z
Publication
Master
EECS
unknown
unknown
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<Abstract>We study the problem of distributed correlation generators wherein n parties wish to simulate unbounded samples from a joint distribution D = Di x D2 X ... x D[subscript n], once they are initialized using randomness sampled from a (possibly different) correlated distribution. We wish to ensure that these samples are computationally indistinguishable from i.i.d. samples from D. Furthermore, we wish to ensure security even against an adversary who corrupts a subset of the parties and obtains their internal (initialization) state. Our contributions are three-fold. First, we define the notion of distributed (noninteractive) correlation generators and show its connection to other cryptographic primitives. Secondly, assuming the existence of indistinguishability obfuscators, we show a construction of distributed correlation generators for a large and natural class of joint distributions that we call conditionally sampleable distributions. Finally, we show a construction for the subclass of additive-spooky distributions assuming private constrained pseudorandom functions (private CPRFs).</Abstract>
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