Private Data Aggregation in Decentralized Networks

Abstract

Privacy-preserving data aggregation is growing in popularity due to the increasing amount of online services depending on user data. This information is privacy-sensitive, warranting the need for protection during data-processing. A wide variety of approaches have been considered to achieve privacy during the processing. Examples include differential privacy, masking, cryptographic techniques (e.g. using homomorphic encryption which enables data processing under encryption). In recent works, several approaches employing the latter privacy-preserving technique has been proposed that is proven to be secure in terms of sensitive data protection. However, the research mainly focuses mostly on efficiency rather than on the selected network topology. In contrast to existing work, we consider a decentralized network, where data can be aggregated without the presence of a central authority, such as an aggregator. We propose two novel protocols based on homomorphic encryption and secret sharing, respectively. Our analyses confirm our claims regarding high efficiency, scalability, and security.