Odin

Abstract

The large spread of sensors and smart devices in urban infrastructures are motivating research in the area of the Internet of Things (IoT) to develop new services and improve citizens’ quality of life. Sensors and smart devices generate large amounts of measurement data from sensing the environment, which is used to enable services such as control of 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 is involved in the computation. In this article we propose ODIN, a novel algorithm allowing information fusion over encrypted data. ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, which prevents distributed agents from having 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 make 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.

Algorithm Design of Early Warning Seatbelt Intelligent Adjustment System Based on Neural Network and Big Data Analysis

“…Characterized by large amount of data, fast speed, multiple types, high value, and strong authenticity, big data analysis refers to the analysis of large-scale data [2]. In this system design, big data analysis technology is mainly applied to the database and software functions as a method for processing and analyzing the initial collected data [3], so that the efficiency of system regulation function is enhanced. e design of intelligent adjustment system not only enables the traditional early warning seatbelt to play its role in the event of an accident but also can fully protect the driver and passengers during the entire driving process, thereby improving the safety factor of automobile [4].…”