IACR News item: 16 June 2015
Iraklis Leontiadis, Kaoutar Elkhiyaoui, Refik Molva, Melek Önen
ePrint Reportfocused on confidentiality issues. That is, the untrusted Aggregator learns only
the aggregation result without divulging individual data inputs. In this paper we
extend the existing models with stronger security requirements. Apart from the
privacy requirements with respect to the individual inputs we ask for unforge-
ability for the aggregate result. We first define the new security requirements of
the model. We also instantiate a protocol for private and unforgeable aggregation
for a non-interactive multi-party environment. I.e, multiple unsynchronized users
owing to personal sensitive information without interacting with each other con-
tribute their values in a secure way: The Aggregator learns the result of a function
without learning individual values and moreover it constructs a proof that is for-
warded to a verifier that will let the latter be convinced for the correctness of the
computation. The verifier is restricted to not communicate with the users. Our
protocol is provably secure in the random oracle model.
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