PUDA - Privacy and Unforgeability for Data Aggregation

Existing work on secure data collection and aggregation is mainly focused on confidentiality issues. That is, ensuring that the untrusted Aggregator learns only the aggregation result without divulging individual data inputs. In this paper however we consider a malicious Aggregator which is not only interested in compromising users’ privacy but also is interested in providing bogus aggregate values. More concretely, we extend existing security models with the requirement of aggregate unforgeability. Moreover, we instantiate an efficient protocol for private and unforgeable data aggregation that allows the Aggregator to compute the sum of users’ inputs without learning individual values and constructs a proof of correct computation that can be verified by any third party. The proposed protocol is provably secure and its communication and computation overhead is minimal.