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Privacy-preserving techniques for distributed computation have been proposedrecently as a promising framework in collaborative inter-domain networkmonitoring. Several different approaches exist to solve such class of problems,e.g., Homomorphic Encryption (HE) and Secure Multiparty Computation (SMC) basedon Shamir's Secret Sharing algorithm (SSS). Such techniques are complete from acomputation-theoretic perspective: given a set of private inputs, it ispossible to perform arbitrary computation tasks without revealing any of theintermediate results. In fact, HE and SSS can operate also on secret inputsand/or provide secret outputs. However, they are computationally expensive anddo not scale well in the number of players and/or in the rate of computationtasks. In this paper we advocate the use of "elementary" (as opposite to"complete") Secure Multiparty Computation (E-SMC) procedures for trafficmonitoring. E-SMC supports only simple computations with private input andpublic output, i.e., it can not handle secret input nor secret (intermediate)output. Such a simplification brings a dramatic reduction in complexity andenables massive-scale implementation with acceptable delay and overhead.Notwithstanding its simplicity, we claim that an E-SMC scheme is sufficient toperform a great variety of computation tasks of practical relevance tocollaborative network monitoring, including, e.g., anonymous publishing and setoperations. This is achieved by combining a E-SMC scheme with data structureslike Bloom Filters and bitmap strings.

Reduce to the Max: A Simple Approach for Massive-Scale Privacy-Preserving Collaborative Network Measurements (Extended Version)