Bogdan Kulynych

Email
  • bogdan.kulynychepfl.ch
    All things academic
  • hellobogdankulynych.me
    All things personal
Github 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.

    See accompanying code that implements poisoning attacks and adversarial examples against a ML-based credit scoring system.

  • 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.

  • mia—an open-source library for evaluating Keras or PyTorch models against attacks on privacy of the training data: membership inference attacks.

  • 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

Academic service