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Distributed computing with privacy

Authors: Himanshu Tyagi;

Distributed computing with privacy

Abstract

A set of terminals that observe correlated data seek to compute functions of the data using interactive public communication. At the same time it is required that this communication, observed by an eavesdropper, does not reveal the value of a private function of the data. In general, the private function and the functions computed by the terminals can be all different. We show that a class of functions are securely computable if and only if the conditional entropy of data given the value of private function is greater than the least rate of interactive communication required for an appropriately chosen multiterminal source coding task. A single-letter formula is provided for this rate in special cases.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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