Distributed Network Privacy using Error Correcting Codes

TL;DR

This paper explores a novel approach to data privacy in distributed networks by using error-correcting codes to limit information sharing and protect signal privacy through intentional symbol errors.

Contribution

It introduces a method of employing linear codes with symbol errors to enforce privacy constraints in unbounded public distributed networks.

Findings

Linear coding limits the number of nodes contributing to a task.

Symbol errors prevent decoding if too many nodes join.

The approach enhances privacy without relying on traditional encryption.

Abstract

Most current distributed processing research deals with improving the flexibility and convergence speed of algorithms for networks of finite size with no constraints on information sharing and no concept for expected levels of signal privacy. In this work we investigate the concept of data privacy in unbounded public networks, where linear codes are used to create hard limits on the number of nodes contributing to a distributed task. We accomplish this by wrapping local observations in a linear code and intentionally applying symbol errors prior to transmission. If many nodes join the distributed task, a proportional number of symbol errors are introduced into the code leading to decoding failure if the code's predefined symbol error limit is exceeded.