  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.
