Description
The pi-calculus was introduced for verifying cryptographic protocols by Abadi and Fournet in 2001. They proposed an equivalence technique, called bisimilarity, useful for verify privacy properties. It is widely acknowledged (cf. Paige and Tarjan 1987), that bisimilarity is more efficient to check than trace equivalence; however, surprisingly, tools based on the applied pi-calculus typically still implement trace equivalence. I suggest this may be attributed to two problems:1. Abadi and Fournet did not publish proofs following conference paper from 2001, until a journal version in 2018 with Blanchet. This perhaps reduced the confidence of the community in bisimilarity. Further to providing proofs, the journal version adjusts definitions to avoid some well known limitations in the original presentation.2. To efficiently implement bisimulation for extensions of the pi-calculus, we typically require a bisimilarity congruence, and no bisimilarity congruence has been proposed for the applied pi-calculus.To address the second point above I propose a bisimilarity congruence for the applied pi-calculus. I argue that the definition I provide is optimal; and show that it is sufficiently strong to verify privacy properties. The definition makes use of recent advances in concurrency theory that were not available prior to LICS 2018. Furthermore, these results lead us to the first sound and complete modal logic for the applied pi-calculus, that can specify attacks if and only if an attack exists.
Infos pratiques
Prochains exposés
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NEAT: A Nile-English Aligned Translation Corpus based on a Robust Methodology for Intent Based Networking and Security
Orateur : Pierre Alain - IUT de Lannion
The rise of Intent Based Networking (IBN) has paved the way for more efficient network and security management, reduced errors, and accelerated deployment times by leveraging AI processes capable of translating natural language intents into policies or configurations. Specialized neural networks could offer a promising solution at the core of translation operations. Still, they require dedicated,[…]-
SoSysec
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Network
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Security policies
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Black-Box Collision Attacks on Widely Deployed Perceptual Hash Functions and Their Consequences
Orateur : Diane Leblanc-Albarel - KU Leuven
Perceptual hash functions identify multimedia content by mapping similar inputs to similar outputs. They are widely used for detecting copyright violations and illegal content but lack transparency, as their design details are typically kept secret. Governments are considering extending the application of these functions to Client-Side Scanning (CSS) for end-to-end encrypted services: multimedia[…]-
Cryptography
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SoSysec
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Malware Detection with AI Systems: bridging the gap between industry and academia
Orateur : Luca Demetrio - University of Genova
With the abundance of programs developed everyday, it is possible to develop next-generation antivirus programs that leverage this vast accumulated knowledge. In practice, these technologies are developed with a mixture of established techniques like pattern matching, and machine learning algorithms, both tailored to achieve high detection rate and low false alarms. While companies state the[…]-
SoSysec
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Intrusion detection
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Machine learning
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CHERIoT RTOS: An OS for Fine-Grained Memory-Safe Compartments on Low-Cost Embedded Devices
Orateur : Hugo Lefeuvre - The University of British Columbia
Embedded systems do not benefit from strong memory protection, because they are designed to minimize cost. At the same time, there is increasing pressure to connect embedded devices to the internet, where their vulnerable nature makes them routinely subject to compromise. This fundamental tension leads to the current status-quo where exploitable devices put individuals and critical infrastructure[…]-
SoSysec
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Compartmentalization
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Operating system and virtualization
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Hardware/software co-design
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Hardware architecture
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