Since 2018, CSF recognizes a subset (between 5% and 20%) of the accepted papers as Distinguished Papers, which are papers that the Program Committee thinks should be read broadly by the CSF community and beyond. The Distinguished Papers are listed by year below.

• CSF 2022
• Cracking the Stateful Nut -- Computational Proofs of Stateful Security Protocols using the Squirrel Proof Assistant
  David Baelde, Stephanie Delaune, Adrien Koutsos, and Solène Moreau
• Applying consensus and replication securely with FLAQR
  Priyanka Mondal, Maximilian Algehed, and Owen Arden
• CSF 2021
• A Quantale of Information
  Sebastian Hunt and David Sands
• SSProve: A Foundational Framework for Modular Cryptographic Proofs in Coq
  Carmine Abate, Philipp G. Haselwarter, Exequiel Rivas, Antoine Van Muylder, Théo Winterhalter, Catalin Hritcu, Kenji Maillard, and Bas Spitters
• Verifying Hyperproperties with TLA
  Leslie Lamport and Fred B. Schneider
• CSF 2020
• A Method for Proving Unlinkability of Stateful Protocols
  David Baelde, Stéphanie Delaune, and Solène Moreau
• Fifty Shades of Ballot Privacy: Privacy against a Malicious Board
  Véronique Cortier, Joseph Lallemand, and Bogdan Warinschi
• Transparent IFC Enforcement: Possibility and (In)Efficiency Results
  Maximilian Algehed and Cormac Flanagan
• CSF 2019
• Journey Beyond Full Abstraction: Exploring Robust Property Preservation for Secure Compilation
  Carmine Abate, Roberto Blanco, Deepak Garg, Catalin Hritcu, Marco Patrignani, and Jérémy Thibault
• Prime, Order Please! Revisiting Small Subgroup and Invalid Curve Attacks on Protocols using Diffie-Hellman
  Cas Cremers and Dennis Jackson
• Automated Verification of Accountability in Security Protocols
  Robert Künnemann, Ilkan Esiyok, and Michael Backes
• CSF 2018
• Secure compilation of side-channel countermeasures: the case of cryptographic “constant-time”
  Gilles Barthe, Benjamin Grégoire, and Vincent Laporte
• Types for Information Flow Control: Labeling Granularity and Semantic Models
  Vineet Rajani and Deepak Garg
• Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
  Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha