Bogdan Kulynych

35 papers A* 5Journal 22Unranked 8
YearRankTypeTitle / Venue / Authors
2026 J jnl
Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation.
CoRR
Bogdan Kulynych, Theresa Stadler, Jean Louis Raisaro, Carmela Troncoso
2025 J jnl
(ϵ, δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees.
CoRR
Juan Felipe Gómez, Bogdan Kulynych, Georgios Kaissis, Jamie Hayes, Borja Balle, Antti Honkela
2025 J jnl
A scoping review of privacy and utility metrics in medical synthetic data.
npj Digit. Medicine
Bayrem Kaabachi, Jérémie Despraz, Thierry Meurers, Karen Otte, Mehmed Halilovic, Bogdan Kulynych, Fabian Prasser, Jean Louis Raisaro
2025 J jnl
Participatory Assessment of Large Language Model Applications in an Academic Medical Center.
CoRR
Giorgia Carra, Bogdan Kulynych, François Bastardot, Daniel E. Kaufmann, Noémie Boillat-Blanco, Jean Louis Raisaro
2025 J jnl
Phantom Anonymization: Adversarial testing for membership inference risks in anonymized health data.
Comput. Biol. Medicine
Thierry Meurers, Mehmed Halilovic, Karen Otte, Jérémie Despraz, Bayrem Kaabachi, Bogdan Kulynych, Jean Louis Raisaro, Fabian Prasser
2025 J jnl
Statistical Inference for Responsiveness Verification.
CoRR
Seung Hyun Cheon, Meredith Stewart, Bogdan Kulynych, Tsui-Wei Weng, Berk Ustun
2025 J jnl
Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy.
CoRR
Bogdan Kulynych, Juan Felipe Gómez, Georgios Kaissis, Jamie Hayes, Borja Balle, Flávio P. Calmon, Jean Louis Raisaro
2024 — conf
Accelerating Clinical Text Annotation in Underrepresented Languages: A Case Study on Text De-Identification.
MIE
He A. Xu, Valentin Loftsson, Bogdan Kulynych, Jean Louis Raisaro
2024 A* conf
Attack-Aware Noise Calibration for Differential Privacy.
NeurIPS
Bogdan Kulynych, Juan Felipe Gómez, Georgios Kaissis, Flávio P. Calmon, Carmela Troncoso
2024 J jnl
Attack-Aware Noise Calibration for Differential Privacy.
CoRR
Bogdan Kulynych, Juan Felipe Gómez, Georgios Kaissis, Flávio du Pin Calmon, Carmela Troncoso
2024 — conf
Evaluating Synthetic Data Augmentation to Correct for Data Imbalance in Realistic Clinical Prediction Settings.
MIE
Bogdan Kulynych, Jérémie Despraz, Christian Simon, Jean Louis Raisaro
2024 A* conf
Prediction without Preclusion: Recourse Verification with Reachable Sets.
ICLR
Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng, Berk Ustun
2024 A* conf
The Fundamental Limits of Least-Privilege Learning.
ICML
Theresa Stadler, Bogdan Kulynych, Michael Gastpar, Nicolas Papernot, Carmela Troncoso
2024 J jnl
The Fundamental Limits of Least-Privilege Learning.
CoRR
Theresa Stadler, Bogdan Kulynych, Nicolas Papernot, Michael Gastpar, Carmela Troncoso
2024 — conf
Tunable Privacy Risk Evaluation of Generative Adversarial Networks.
MIE
Bogdan Kulynych, Jérémie Despraz, Jean Louis Raisaro
2023 A* conf
Adversarial Robustness for Tabular Data through Cost and Utility Awareness.
NDSS
Klim Kireev, Bogdan Kulynych, Carmela Troncoso
2023 — conf
Arbitrary Decisions Are a Hidden Cost of Differentially Private Training.
EWAF
Bogdan Kulynych, Hsiang Hsu, Carmela Troncoso, Flávio P. Calmon
2023 — conf
Arbitrary Decisions are a Hidden Cost of Differentially Private Training.
FAccT
Bogdan Kulynych, Hsiang Hsu, Carmela Troncoso, Flávio P. Calmon
2023 J jnl
Arbitrary Decisions are a Hidden Cost of Differentially-Private Training.
CoRR
Bogdan Kulynych, Hsiang Hsu, Carmela Troncoso, Flávio P. Calmon
2023 J jnl
Prediction without Preclusion: Recourse Verification with Reachable Sets.
CoRR
Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng, Berk Ustun
2022 J jnl
Adversarial Robustness for Tabular Data through Cost and Utility Awareness.
CoRR
Klim Kireev, Bogdan Kulynych, Carmela Troncoso
2022 J jnl
Disparate Vulnerability to Membership Inference Attacks.
Proc. Priv. Enhancing Technol.
Bogdan Kulynych, Mohammad Yaghini, Giovanni Cherubin, Michael Veale, Carmela Troncoso
2022 J jnl
What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning.
CoRR
Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok, Preetum Nakkiran
2022 A* conf
What You See is What You Get: Principled Deep Learning via Distributional Generalization.
NeurIPS
Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok, Preetum Nakkiran
2021 J jnl
Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks.
CoRR
Kendra Albert, Maggie K. Delano, Bogdan Kulynych, Ram Shankar Siva Kumar
2021 J jnl
Exploring Data Pipelines through the Process Lens: a Reference Model forComputer Vision.
CoRR
Agathe Balayn, Bogdan Kulynych, Seda F. Gürses
2020 — conf
POTs: protective optimization technologies.
FAT*
Bogdan Kulynych, Rebekah Overdorf, Carmela Troncoso, Seda F. Gürses
2019 J jnl
Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning.
CoRR
Mohammad Yaghini, Bogdan Kulynych, Carmela Troncoso
2019 — conf
zksk: A Library for Composable Zero-Knowledge Proofs.
WPES@CCS
Wouter Lueks, Bogdan Kulynych, Jules Fasquelle, Simon Le Bail-Collet, Carmela Troncoso
2019 J jnl
zksk: A Library for Composable Zero-Knowledge Proofs.
CoRR
Wouter Lueks, Bogdan Kulynych, Jules Fasquelle, Simon Le Bail-Collet, Carmela Troncoso
2018 — conf
ClaimChain: Improving the Security and Privacy of In-band Key Distribution for Messaging.
WPES@CCS
Bogdan Kulynych, Wouter Lueks, Marios Isaakidis, George Danezis, Carmela Troncoso
2018 J jnl
Evading classifiers in discrete domains with provable optimality guarantees.
CoRR
Bogdan Kulynych, Jamie Hayes, Nikita Samarin, Carmela Troncoso
2018 J jnl
Questioning the assumptions behind fairness solutions.
CoRR
Rebekah Overdorf, Bogdan Kulynych, Ero Balsa, Carmela Troncoso, Seda F. Gürses
2017 J jnl
ClaimChain: Decentralized Public Key Infrastructure.
CoRR
Bogdan Kulynych, Marios Isaakidis, Carmela Troncoso, George Danezis
2017 J jnl
Feature importance scores and lossless feature pruning using Banzhaf power indices.
CoRR
Bogdan Kulynych, Carmela Troncoso