Privacy-Preserving Multi-Party Analytics over Arbitrarily Partitioned Data

Data-driven business processes are gaining popularity among enterprises now-a-days. In many situations, multiple parties would share data towards a common goal if it were possible to simultaneously protect the privacy of the individuals and organizations described in the data. Existing solutions for multi-party analytics require parties to transfer their raw data to a trusted mediator, who then performs the desired analysis on the global data, and shares the results with the parties. Unfortunately, such a solution does not fit many applications where privacy is a strong concern such as healthcare, finance and the internet-of-things. Motivated by the increasing demands for data privacy, in this paper, we study the problem of privacy-preserving multi-party analytics, where the goal is to enable analytics on multi-party data without compromising the data privacy of each individual party. We propose a secure gradient descent algorithm that enables analytics on data that is arbitrarily partitioned among multiple parties. The proposed algorithm is generic and applies to a wide class of machine learning problems. We demonstrate our solution for a popular use-case (i.e., regression), and evaluate the performance of the proposed secure solution in terms of accuracy, latency and communication cost. We also perform a scalability analysis to evaluate the performance of the proposed solution as the data size and the number of parties increase.