PUDA - Privacy and unforgeability for data aggregation

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 unforgeability 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 personal 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.