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.