Distributed correlation generators

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).

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Hui, Joseph,S.M.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Vinod Vaikuntanathan.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122871

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122871