A Keyless Gossip Algorithm Providing Light-Weight Data Privacy for Prosumer Markets

We propose secure multi-party computation techniques for the distributed computation of the average using a privacy-preserving extension of gossip algorithms. While recently there has been mainly research on the side of gossip algorithms (GA) for data aggregation itself, to the best of our knowledge, the aforementioned research line does not take into consideration the privacy of the entities involved. More concretely, it is our objective to not reveal a node's private input value to any other node in the network, while still computing the average in a fully-decentralized fashion. Not revealing in our setting means that an attacker gains only minor advantage when guessing a node's private input value. We precisely quantify an attacker's advantage when guessing as a mean for the level of data privacy leakage of a node's contribution. Our results show that by perturbing the input values of each participating node with pseudo-random noise with appropriate statistical properties i) only a minor and configurable leakage of private information is revealed, by at the same time ii) providing a good average approximation at each node. Our approach can be applied to a decentralized prosumer market, in which participants act as energy consumers or producers or both, referred to as prosumers.