Aggregation of time-series data offers the possibility to learn certain statistics over data periodically uploaded by different sources. In case of privacy sensitive data, it is desired to hide every data provider's individual values from the other participants (including the data aggregator). Existing privacy preserving time-series data aggregation schemes focus on the sum as aggregation means, since it is the most essential statistics used in many applications such as smart metering, participatory sensing, or appointment scheduling. However, all existing schemes have an important drawback: they do not provide verifiable outputs, thus users have to trust the data aggregator that it does not output fake values.We propose a publicly verifiable data aggregation scheme for privacy preserving time-series data summation. We prove its security and verifiability under the XDH assumption and a widely used, strong variant of the Co-CDH assumption. Moreover, our scheme offers low computation complexity on the users' side, which is essential in many applications.

Within this paper a secure peer-to-peer (P2P) based storage system is presented with additional features and some simulation results. The main motivation for such a system is to provide the functionality of a cloud storage without having to rely on central servers or large datacenters. Users of the proposed P2P storage system are able to store their encrypted files at other users and vice versa, providing availability at the cost of some extra storage space. The system motivates fair behaviour by using reputation points. We investigate the possible optimal user behaviours and the bandwidth/space trade-offs based on simulation results.