Anthony Genot

h-index12
2papers

2 Papers

8.4CRMar 17
Synchronized DNA sources for unconditionally secure cryptography

Sandra Jaudou, Hélène Gasnier, Elias Boudjella et al.

Secure communication is the cornerstone of modern infrastructures, yet achieving unconditional security -resistant to any computational attack- remains a fundamental challenge. The One-Time Pad (OTP), proven by Shannon to offer perfect secrecy, requires a shared random key as long as the message, used only once. However, distributing large keys over long distances has been impractical due to the lack of secure and scalable sharing options. Here, we introduce a DNA-based cryptographic primitive that leverages random pools of synthetic DNA to install a synchronized entropy source between distant parties. Our approach uses duplicated DNA molecules -comprising random index-payload pairs- as a shared secret. These molecules are locally sequenced and digitized to generate a common binary mask for OTP encryption, achieving unconditional security without relying on computational assumptions. We experimentally demonstrate this protocol between Tokyo and Paris, using in-house sequencing, generating a shared secret mask of $\sim$ 400 Mb with a residual error rate to achieve the usual overall decryption failure rate of $2^{-128}$. The min-entropy of the binary mask meets the most recent National Institute of Standards and Technology requirements (SP 800-90B), and is comparable to that of approved cryptographic random number generators. Critically, our system can resist two types of adversarial interference through molecular copy-number statistics, providing an additional layer of security reminiscent of Quantum Key Distribution, but without distance limitations. This work establishes DNA as a scalable entropy source for long-distance OTP, enabling high-throughput and secure communications in sensitive contexts. By bridging molecular biology and cryptography, DNA-based key distribution opens a promising new route toward unconditional security in global communication networks.

RONov 18, 2024
Signaling and Social Learning in Swarms of Robots

Leo Cazenille, Maxime Toquebiau, Nicolas Lobato-Dauzier et al.

This paper investigates the role of communication in improving coordination within robot swarms, focusing on a paradigm where learning and execution occur simultaneously in a decentralized manner. We highlight the role communication can play in addressing the credit assignment problem (individual contribution to the overall performance), and how it can be influenced by it. We propose a taxonomy of existing and future works on communication, focusing on information selection and physical abstraction as principal axes for classification: from low-level lossless compression with raw signal extraction and processing to high-level lossy compression with structured communication models. The paper reviews current research from evolutionary robotics, multi-agent (deep) reinforcement learning, language models, and biophysics models to outline the challenges and opportunities of communication in a collective of robots that continuously learn from one another through local message exchanges, illustrating a form of social learning.