Data Aggregation for Privacy Protection of Data Streams Between Autonomous IoT Networks

Abstract

Many IoT applications rely on data streams, flowing from producers to consumers. Typically, Named Data Networking has been designed to manipulate directly data chunks, and is particularly relevant in IoT networks. However, in multi-tenant networks, privacy is a major concern, and producers may refuse to share the personal data they generate with non trusted stakeholders. To guarantee k-anonymity, producers can require their data to be aggregated with the one of other producers. We propose here a routing scheme based on aggregation, relying on a pub-sub approach. By appropriately constructing and querying the set of offers, i.e. the list of data streams that are collected, aggregated and transformed together, our routing aggregation scheme provides a privacy aware large-scale interconnection, where the consumer does not access directly to individual measurements. Our performance evaluation highlights the flexibility of our solution to accommodate a large set of queries, while still respecting privacy.