With the advent of networking applications collecting user data on
a massive scale, the privacy of individual users appears to be a major concern.
The main challenge is the design of a solution that allows the data analyzer to
compute global statistics over the set of individual inputs that are protected by
some confidentiality mechanism. Joye et al.  recently suggested a solution
that allows a centralized party to compute the sum of encrypted inputs collected
through a smart metering network. The main shortcomings of this solution are
its reliance on a trusted dealer for key distribution and the need for frequent key
updates. In this paper we introduce a secure protocol for aggregation of timeseries
data that is based on the Joye et al.  scheme and in which the main
shortcomings of the latter, namely, the requirement for key updates and for the
trusted dealer are eliminated. As such, during the protocol execution none of the
parties apart from the users themselves are aware of the secret keys. Moreover
our scheme supports a dynamic group management, whereby as opposed to Joye
et al.  leave and join operations do not trigger a key update at the users.