To build robust secure channels for information exchange, distributed computing systems must generate and handle high-entropy secret keys. However, solutions to generate those high-entropy keys such as Physical Unclonable Functions or sensing devices are very dependent on the environment and the hardware performance. Thus, keys may not achieve the expected entropy or show uncontrolled behaviors that may prevent communicating remote nodes to synchronize with a shared key. Therefore, new solutions are needed to enable distributed computing nodes to generate high-entropy keys in a lightweight, consistent, and robust manner. In this paper we propose a federated algorithm to address this challenge. Remote nodes are provided with different physical devices to initialize with a random configuration a Fibonacci random number generator. The parameter set describing the configuration of the key generator is locally encoded using a gradient function and sent to an edge computing manager where different encoded configurations coming from different remote nodes are collected. The edge computing manager combines all these configurations considering different weights and an optimization target function based on the definition of mutual information. An experimental validation is also provided. Simulation tools are employed, and results show the long-term average entropy increases up to 23% when using the proposed solution.
To build robust secure channels for information exchange, distributed computing systems must generate and handle high-entropy secret keys. However, solutions to generate those high-entropy keys such as Physical Unclonable Functions or sensing devices are very dependent on the environment and the hardware performance. Thus, keys may not achieve the expected entropy or show uncontrolled behaviors that may prevent communicating remote nodes to synchronize with a shared key. Therefore, new solutions are needed to enable distributed computing nodes to generate high-entropy keys in a lightweight, consistent, and robust manner. In this paper we propose a federated algorithm to address this challenge. Remote nodes are provided with different physical devices to initialize with a random configuration a Fibonacci random number generator. The parameter set describing the configuration of the key generator is locally encoded using a gradient function and sent to an edge computing manager where different encoded configurations coming from different remote nodes are collected. The edge computing manager combines all these configurations considering different weights and an optimization target function based on the definition of mutual information. An experimental validation is also provided. Simulation tools are employed, and results show the long-term average entropy increases up to 23% when using the proposed solution. Read More
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