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Distributed Function Computation with Confidentiality
A set of terminals observe correlated data and seek to compute functions of
the data using interactive public communication. At the same time, it is
required that the value of a private function of the data remains concealed
from an eavesdropper observing this communication. In general, the private
function and the functions computed by the nodes 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 a related
multiterminal source-coding task. A single-letter formula is provided for this
rate in special cases.Comment: To Appear in IEEE JSAC: In-Network Computation: Exploring the
Fundamental Limits, April 201
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