Elementary secure-multiparty computation for massive-scale collaborative network monitoring: A quantitative assessment

Recently, Secure-Multiparty Computation (SMC) has been proposed as an approach to enable inter-domain network monitoring while protecting the data of individual ISPs. The SMC family includes many different techniques and variants, featuring different forms of ‘‘security’’, i.e., against different types of attack (er), and with different levels of computation complexity and communication overhead. In the context of collaborative network monitoring, the rate and volume of network data to be (securely) processed is massive, and the number of participating players is large, therefore scalability is a primary requirement. To preserve scalability one must sacrifice other requirement, like verifiability and computational completeness that, however, are not critical in our context. In this paper we consider two possible schemes: the Shamir’s Secret Sharing (SSS), based on polynomial interpolation on prime fields, and the Globally-Constrained Randomization (GCR) scheme based on simple blinding. We address various system-level aspects and quantify the achievable performance of both schemes. A prototype version of GCR has been implemented as an extension of SEPIA, an open-source SMC library developed at ETH Zurich that supports SSS natively. We have performed a number of controlled experiments in distributed emulated scenarios for comparing SSS and GCR performance. Our results show that additions via GCR are faster than via SSS, that the relative performance gain increases when scaling up the data volume and/or number of participants, and when network conditions get worse. Furthermore, we analyze the performance degradation due to sudden node failures, and show that it can be satisfactorily controlled by containing the fault probability below a reasonable level.