Explicit Randomness is not Necessary when Modeling Probabilistic Encryption
Véronique Cortier, Heinrich Hördegen, Bogdan Warinschi
Abstract
Although good encryption functions are probabilistic, most symbolic models do not capture this aspect explicitly. A typical solution, recently used to prove the soundness of such models with respect to computational ones, is to explicitly represent the dependency of ciphertexts on random coins as labels. In order to make these label-based models useful, it seems natural to try to extend the underlying decision procedures and the implementation of existing tools. In this paper we put forth a more practical alternative based on the following soundness theorem. We prove that for a large class of security properties (that includes rather standard formulations for secrecy and authenticity properties), security of protocols in the simpler model implies security in the label-based model. Combined with the soundness result of (?) our theorem enables the translation of security results in unlabeled symbolic models to computational security.
Create a lesson
Related papers
RedEvoAgent: Automatic Red-Teaming Agent with Experience-Driven Skill Evolution
Junjie Zhang, Hui Liu, Kecheng Chen et al.
Beyond F1: Evaluating Coverage and Failure Recovery in AI Model Security Scanners
Qianlong Lan, Vinothini Pandurangan, Anuj Kaul et al.
When Context Gets Root: Privilege Escalation in LLM Harnesses
Xingbang He, Yuanwei Chen, Yi Qian et al.
Low-ASR Backdoors: Exploiting Attack Success Rate Reduction and Attacker-Defender Asymmetry
Arham Riaz, Ting Yu
SPA: Securing Persistent LLM Agents Across Queries with Plan-First Information-Flow Control
Dylan Girrens, Guangjing Wang
From Security Events to Conflict States: A Three-layer Cyber Defense Scenario Model for Enhanced Cyber Situational Awareness
Miguel Requena Micó, Mario Fernandez-Tarraga, Daniel Díaz-López et al.