MPCC - Generic Secure Multi-Party Computation in Centralized Cloud-based Environments
ACM Proceedings of the 3rd International Conference on Big Data Research (ICBDR2019)
Comparing business KPIs with other market participants through benchmarking is a means for companies to optimize costs. Those collaborative optimizations require data of all participating actors that might include business secrets and, therefore, must be kept private in many cases. This demonstrates the demand for privacy-preserving Big Data analytics. Over the last decades, a variety of mechanisms and protocols that enable privacy-preserving collaborative computations have been presented such as trusted third party (TTP) approaches or secure multi-party computation (MPC).
However, existing solutions for privacy-preserving benchmarking often only compute a fixed set of arithmetic functions and usually follow a decentralized communication scheme among all peers. We will instead investigate a generic secure MPC system that (1) can compute more general functions, (2) discuss benefits of a service-provider oriented centralized setup and (3) determine the class of arithmetic functions that the selected secure computation mechanism can compute feasibly in practice.
Our solution proposes an architecture that can calculate functions containing an arbitrary number of additions, subtractions and multiplications, which is capable to operate in scalable cloud environments with also a larger number of peers with the objective to deliver computation results within 24 hours.
Kaiser, Philip; Langer, André; Gaedke, Martin: MPCC - Generic Secure Multi-Party Computation in Centralized Cloud-based Environments. ACM Proceedings of the 3rd International Conference on Big Data Research (ICBDR2019), pp. 60-66, 2019.