ClaimChain: Decentralized Public Key Infrastructure. - Bogdan Kulynych; Marios Isaakidis; Carmela Troncoso; George Danezis (CoRR, 2017)

Feature importance scores and lossless feature pruning using Banzhaf power indices. - Bogdan Kulynych; Carmela Troncoso (CoRR, 2017)

ClaimChain: Improving the Security and Privacy of In-band Key Distribution for Messaging. - Bogdan Kulynych; Wouter Lueks; Marios Isaakidis; George Danezis; Carmela Troncoso (WPES@CCS, 2018)

Evading classifiers in discrete domains with provable optimality guarantees. - Bogdan Kulynych; Jamie Hayes; Nikita Samarin; Carmela Troncoso (CoRR, 2018)

Questioning the assumptions behind fairness solutions. - Rebekah Overdorf; Bogdan Kulynych; Ero Balsa; Carmela Troncoso; Seda F. Gürses (CoRR, 2018)

zksk: A Library for Composable Zero-Knowledge Proofs. - Wouter Lueks; Bogdan Kulynych; Jules Fasquelle; Simon Le Bail-Collet; Carmela Troncoso (WPES@CCS, 2019)

Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning. - Mohammad Yaghini; Bogdan Kulynych; Carmela Troncoso (CoRR, 2019)

POTs: protective optimization technologies. - Bogdan Kulynych; Rebekah Overdorf; Carmela Troncoso; Seda F. Gürses (FAT*, 2020)

Exploring Data Pipelines through the Process Lens: a Reference Model forComputer Vision. - Agathe Balayn; Bogdan Kulynych; Seda F. Gürses (CoRR, 2021)

Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks. - Kendra Albert; Maggie K. Delano; Bogdan Kulynych; Ram Shankar Siva Kumar (CoRR, 2021)

Epidose: A privacy-preserving epidemic dosimeter based on contact tracing. - Diomidis Spinellis; Stefanos Georgiou; Johan Herland; Mathias Payer; Wouter Lueks; Bogdan Kulynych; Filip Muki Dobranic; konasimakopoulos (Zenodo, 2021)

What You See is What You Get: Principled Deep Learning via Distributional Generalization. - Bogdan Kulynych; Yao-Yuan Yang; Yaodong Yu; Jaroslaw Blasiok; Preetum Nakkiran (NeurIPS, 2022)

What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning. - Bogdan Kulynych; Yao-Yuan Yang; Yaodong Yu; Jaroslaw Blasiok; Preetum Nakkiran (CoRR, 2022)

Disparate Vulnerability to Membership Inference Attacks. - Bogdan Kulynych; Mohammad Yaghini; Giovanni Cherubin; Michael Veale; Carmela Troncoso (Proc. Priv. Enhancing Technol., 2022)

Arbitrary Decisions Are a Hidden Cost of Differentially Private Training. - Bogdan Kulynych; Hsiang Hsu; Carmela Troncoso; Flávio P. Calmon (EWAF, 2023)

Adversarial Robustness for Tabular Data through Cost and Utility Awareness. - Klim Kireev; Bogdan Kulynych; Carmela Troncoso (NDSS, 2023)

Prediction without Preclusion: Recourse Verification with Reachable Sets. - Avni Kothari; Bogdan Kulynych; Tsui-Wei Weng; Berk Ustun (ICLR, 2024)

The Fundamental Limits of Least-Privilege Learning. - Theresa Stadler; Bogdan Kulynych; Michael Gastpar; Nicolas Papernot; Carmela Troncoso (ICML, 2024)

Tunable Privacy Risk Evaluation of Generative Adversarial Networks. - Bogdan Kulynych; Jérémie Despraz; Jean Louis Raisaro (MIE, 2024)

Evaluating Synthetic Data Augmentation to Correct for Data Imbalance in Realistic Clinical Prediction Settings. - Bogdan Kulynych; Jérémie Despraz; Christian Simon; Jean Louis Raisaro (MIE, 2024)

Accelerating Clinical Text Annotation in Underrepresented Languages: A Case Study on Text De-Identification. - He A. Xu; Valentin Loftsson; Bogdan Kulynych; Jean Louis Raisaro (MIE, 2024)

Attack-Aware Noise Calibration for Differential Privacy. - Bogdan Kulynych; Juan Felipe Gómez; Georgios Kaissis; Flávio P. Calmon; Carmela Troncoso (NeurIPS, 2024)

Phantom Anonymization: Adversarial testing for membership inference risks in anonymized health data. - Thierry Meurers; Mehmed Halilovic; Karen Otte; Jérémie Despraz; Bayrem Kaabachi; Bogdan Kulynych; Jean Louis Raisaro; Fabian Prasser (Comput. Biol. Medicine, 2025)

A scoping review of privacy and utility metrics in medical synthetic data. - Bayrem Kaabachi; Jérémie Despraz; Thierry Meurers; Karen Otte; Mehmed Halilovic; Bogdan Kulynych; Fabian Prasser; Jean Louis Raisaro (npj Digit. Medicine, 2025)

Participatory Assessment of Large Language Model Applications in an Academic Medical Center. - Giorgia Carra; Bogdan Kulynych; François Bastardot; Daniel E. Kaufmann; Noémie Boillat-Blanco; Jean Louis Raisaro (CoRR, 2025)

(ϵ, δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees. - Juan Felipe Gómez; Bogdan Kulynych; Georgios Kaissis; Jamie Hayes; Borja Balle; Antti Honkela (CoRR, 2025)

Statistical Inference for Responsiveness Verification. - Seung Hyun Cheon; Meredith Stewart; Bogdan Kulynych; Tsui-Wei Weng; Berk Ustun (CoRR, 2025)

Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy. - Bogdan Kulynych; Juan Felipe Gómez; Georgios Kaissis; Jamie Hayes; Borja Balle; Flávio P. Calmon; Jean Louis Raisaro (CoRR, 2025)