Publication

Scalable and Secure Aggregation in Distributed Networks

Rachid Guerraoui
2011
Working Papers

Abstract

We consider the problem of computing an aggregation function in a \emph{secure} and \emph{scalable} way. Whereas previous distributed solutions with similar security guarantees have a communication cost of $O(n^3)$ , we present a distributed protocol that requires only a communication complexity of $O(n\log^3 n)$ , which we prove is near-optimal. Our protocol ensures perfect security against a computationally-bounded adversary, tolerates $(1/2-\epsilon)n$ malicious nodes for any constant $1/2 > \epsilon > 0$ (not depending on $n$ ), and outputs the exact value of the aggregated function with high probability.

Official source

https://infoscience.epfl.ch/entities/publication/f98719d0-8607-4c8c-96ae-5bbad09d715f

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Rachid Guerraoui
2011
Working Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/f98719d0-8607-4c8c-96ae-5bbad09d715f

About this result

Related concepts (32)

Distributed data store

A distributed data store is a computer network where information is stored on more than one node, often in a replicated fashion. It is usually specifically used to refer to either a distributed database where users store information on a number of nodes, or a computer network in which users store information on a number of peer network nodes. Distributed databases are usually non-relational databases that enable a quick access to data over a large number of nodes.

Communication complexity

In theoretical computer science, communication complexity studies the amount of communication required to solve a problem when the input to the problem is distributed among two or more parties. The study of communication complexity was first introduced by Andrew Yao in 1979, while studying the problem of computation distributed among several machines. The problem is usually stated as follows: two parties (traditionally called Alice and Bob) each receive a (potentially different) -bit string and .

Secure multi-party computation

Secure multi-party computation (also known as secure computation, multi-party computation (MPC) or privacy-preserving computation) is a subfield of cryptography with the goal of creating methods for parties to jointly compute a function over their inputs while keeping those inputs private. Unlike traditional cryptographic tasks, where cryptography assures security and integrity of communication or storage and the adversary is outside the system of participants (an eavesdropper on the sender and receiver), the cryptography in this model protects participants' privacy from each other.

Related publications (1)

Scalable and Secure Aggregation in Distributed Networks

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