Bogdan Kulynych

Graduate student at EPFL Security and Privacy Engineering Lab (SPRING). Former intern at Google and CERN.

I am interested in the intersection of privacy, security, and machine learning.

Projects and publications

Protective optimization technologies

• Protective optimization technologies (POTs)—a concept of developing technologies that counteract harmful optimization systems from outside.

  Rebekah Overdorf, Bogdan Kulynych, Ero Balsa, Carmela Troncoso, Seda Gürses. POTs: Protective Optimization Technologies. Under submission.

• textfool—a tool for evading text classifiers, with the goal of countering privacy-invasive classifiers.

Provable security and privacy for machine learning

• Provably minimal-effort evasion attacks in constrained discrete domains. More details coming soon.

• Power indices for machine learning models—game-theoretic tools that can be used to prove that certain features can not influence a classifier’s decision.

  Bogdan Kulynych, Carmela Troncoso. Feature importance scores and lossless feature pruning using Banzhaf power indices. NIPS 2017 Symposium on Interpretable Machine Learning, December 7, 2017, Long Beach, CA, USA.

Privacy-preserving cryptographic data structures

• ClaimChain–a system and data structure for decentralized privacy-preserving public key distribution. It has a working prototype, and is being tested by the Autocrypt opportunistic email encryption initiative.

  Bogdan Kulynych, Wouter Lueks, Marios Isaakidis, George Danezis, and Carmela Troncoso. ClaimChain: Improving the Security and Privacy of In-band Key Distribution for Messaging. In 2018 Workshop on Privacy in the Electronic Society (WPES’18), October 15, 2018, Toronto, ON, Canada.

Academic service

• External reviewer at Privacy-Enhancing Technologies Symposium 2019
• Program committee member at the Internet Science Conference Workshop on Encryption, Blockchains, and Personal Data, 2018