  The large spread of sensors and smart devices in urban infrastructures are
motivating research in the area of Internet of Thing (IoT), to develop new
services and improve citizens' quality of life. Sensors and smart devices
generate large amount of measurement data from sensing the environment, which
is used to enable services, such as control power consumption or traffic
density. To deal with such a large amount of information, and provide accurate
measurements, service providers can adopt information fusion, which, given the
decentralized nature of urban deployments, can be performed by means of
consensus algorithms. These algorithms allow distributed agents to
(iteratively) compute linear functions on the exchanged data, and take
decisions based on the outcome, without the need for the support of a central
entity. However, the use of consensus algorithms raises several security
concerns, especially when private or security critical information are involved
in the computation. This paper proposes ODIN, a novel algorithm that allows
information fusion over encrypted data. ODIN is a privacy-preserving extension
of the popular consensus gossip algorithm, that prevents distributed agents
have direct access to the data while they iteratively reach consensus; agents
cannot access even the final consensus value, but can only retrieve partial
information, e.g., a binary decision. ODIN uses efficient additive obfuscation
and proxy re-encryption during the update steps, and Garbled Circuits to take
final decisions on the obfuscated consensus. We discuss the security of our
proposal, and show its practicability and efficiency on real-world resource
constrained devices, developing a prototype implementation for Raspberry Pi
devices.
