Existing work on data collection and analysis for aggregation is mainly
focused 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 multiple independent users. I.e, multiple unsynchronized users owing to per-
sonal sensitive information without interacting with each other, contribute 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 forwarded
to a verifier that will convince the latter for the correctness of the computation.
Our protocol is provably secure in the random oracle model.
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