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Distributed Task Privacy for Aggregation Using Linear Codes
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PRE
AI
DOI：10.1109/TSIPN.2021.3112928.png)
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Abstract
En 中文
Information privacy in modern Internet of Things (IoT) environments is necessary for widespread adoption and public trust of collaborative data processing. Most current distributed privacy results ensure local node observations are not accessible by neighbouring nodes while still solving collaborative tasks. In this work we develop the distinct concept of distributed task privacy in unbounded public networks, where linear codes are used to create arbitrary 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 or erasures prior to transmission. If many nodes join a distributed task, a proportional number of symbol errors and erasures are introduced into the aggregated code leading to decoding failure if the code's predefined symbol error/erasure limit is exceeded.
Keywords:
Distributed
privacy
unbounded
public networks
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