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 codes predefined symbol error limit is exceeded.
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