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Related papers: Probabilistic annotations for protocol models

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Interactive proof systems whose verifiers are constant-space machines have interesting features that do not have counterparts in the better studied case where the verifiers operate under reasonably large space bounds. The language…

Computational Complexity · Computer Science 2025-12-17 M. Utkan Gezer , A. C. Cem Say

We delineate a methodology for the specification and verification of flow security properties expressible in the opacity framework. We propose a logic, OpacTL , for straightforwardly expressing such properties in systems that can be…

Cryptography and Security · Computer Science 2022-06-30 Chunyan Mu , David Clark

Relational Hoare logics extend the applicability of modular, deductive verification to encompass important 2-run properties including dependency requirements such as confidentiality and program relations such as equivalence or similarity…

Logic in Computer Science · Computer Science 2022-07-19 David A. Naumann

How predictable a word is can be quantified in two ways: using human responses to the cloze task or using probabilities from language models (LMs).When used as predictors of processing effort, LM probabilities outperform probabilities…

Computation and Language · Computer Science 2026-05-27 Sathvik Nair , Byung-Doh Oh

We address the problem of quantifying the cryptographic content of probability distributions, in relation to an application to secure multi-party sampling against a passive t-adversary. We generalize a recently introduced notion of assisted…

Information Theory · Computer Science 2015-04-21 Pradeep Kr. Banerjee

The investigation of Bell nonlocality traditionally relies on joint probabilities of observing certain measurement outcomes. In this work we explore a possibilistic approach, where only patterns of possible outcomes matter, and apply it to…

Quantum Physics · Physics 2022-09-21 Antoine Restivo , Nicolas Brunner , Denis Rosset

Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually interpretable and they can be learned effectively from the…

Artificial Intelligence · Computer Science 2014-01-17 Tobias Lang , Marc Toussaint

The aim of this thesis project is to investigate the bit commitment protocol in the framework of operational probabilistic theories. In particular a careful study is carried on the feasibility of bit commitment in the non-local boxes…

Quantum Physics · Physics 2021-01-25 Lorenzo Giannelli

Bayesian networks provide a probabilistic semantics for qualitative assertions about likelihood. A qualitative reasoner based on an algebra over these assertions can derive further conclusions about the influence of actions. While the…

Artificial Intelligence · Computer Science 2013-04-12 Michael P. Wellman

We explore a fuzzy modal logic that can formalise probabilistic reasoning about actions and knowledge. In particular, we deal with contexts involving statements about events expressed via modal formulas, e.g., "after doing $a$, the…

Logic in Computer Science · Computer Science 2026-04-27 Daniil Kozhemiachenko , Igor Sedlár

Hoare logic is a foundation of axiomatic semantics of classical programs and it provides effective proof techniques for reasoning about correctness of classical programs. To offer similar techniques for quantum program verification and to…

Quantum Physics · Physics 2009-06-26 Mingsheng Ying

It is becoming increasingly apparent that probabilistic approaches can overcome conservatism and computational complexity of the classical worst-case deterministic framework and may lead to designs that are actually safer. In this paper we…

Applications · Statistics 2008-11-01 Xinjia Chen , Kemin Zhou , Jorge L. Aravena

Neural networks are becoming increasingly prevalent in software, and it is therefore important to be able to verify their behavior. Because verifying the correctness of neural networks is extremely challenging, it is common to focus on the…

Machine Learning · Computer Science 2019-02-19 Ravi Mangal , Aditya V. Nori , Alessandro Orso

This paper argues that a combined treatment of probabilities, time and actions is essential for an appropriate logical account of the notion of probability; and, based on this intuition, describes an expressive probabilistic temporal logic…

Logic in Computer Science · Computer Science 2017-10-10 Bruno Woltzenlogel Paleo

In the symbolic verification of cryptographic protocols, a central problem is deciding whether a protocol admits an execution which leaks a designated secret to the malicious intruder. Rusinowitch & Turuani (2003) show that, when…

Logic in Computer Science · Computer Science 2024-01-29 R Ramanujam , Vaishnavi Sundararajan , S P Suresh

We provide a way to ease the verification of programs whose state evolves monotonically. The main idea is that a property witnessed in a prior state can be soundly recalled in the current state, provided (1) state evolves according to a…

Programming Languages · Computer Science 2017-11-10 Danel Ahman , Cédric Fournet , Catalin Hritcu , Kenji Maillard , Aseem Rastogi , Nikhil Swamy

A new scheme of probabilistic subgroup-related encryption is introduced. Some applications of this scheme based on the RSA, Diffie-Hellman and ElGamal encryption algorithms are described. Security assumptions and main advantages of this…

Cryptography and Security · Computer Science 2016-03-08 Vitalii Roman'kov

Building on ideas of Gurevich and Shelah for the G\"odel Class, we present a new probabilistic proof of the finite model property for the Guarded Fragment of First-Order Logic. Our proof is conceptually simple and yields the optimal…

Logic in Computer Science · Computer Science 2026-05-29 Oskar Fiuk

Deductive verification techniques based on program logics (i.e., the family of Floyd-Hoare logics) are a powerful approach for program reasoning. Recently, there has been a trend of increasing the expressive power of such logics by…

Logic in Computer Science · Computer Science 2021-01-27 Marco Gaboardi , Shin-ya Katsumata , Dominic Orchard , Tetsuya Sato

When language models are trained by reinforcement learning (RL) to write probabilistic programs, they can artificially inflate their marginal-likelihood reward by producing programs whose data distribution fails to normalise instead of…

Machine Learning · Computer Science 2026-03-26 Jacek Karwowski , Younesse Kaddar , Zihuiwen Ye , Nikolay Malkin , Sam Staton
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