A distributed protocol for privacy preserving aggregation

Abstract

Techniques that combine and analyze data collected from multiple partners are very useful for distributed collaborative applications. Such collaborative computations could occur between trusted partners, between partially trusted partners, or between competitors. Therefore preserving privacy is an important issue in this context. This paper presents a distributed protocol for privacy-preserving aggregation to enable computing a class of aggregation functions that can be expressed as Abelian group. The aim is to ensure participants privacy such that their inputs are not disclosed to any other entity be it trusted or not. The proposed protocol is based on an overlay structure that enables secret sharing without the need of any central authority or heavyweight cryptography. © 2013 Springer-Verlag.