Publications by Bogdan Kulynych

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

Assoc Computing Machinery2023

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Bogdan Kulynych

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

EPFL2023

POTs: Protective Optimization Technologies

Carmela González Troncoso, Bogdan Kulynych, Rebekah Jean Overdorf

Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a n ...

ASSOC COMPUTING MACHINERY2020

zksk: A Library for Composable Zero-Knowledge Proofs

Carmela González Troncoso, Wouter Lueks, Bogdan Kulynych

Zero-knowledge proofs are an essential building block in many privacy-preserving systems. However, implementing these proofs is tedious and error-prone. In this paper, we present zksk, a well-documented Python library for defining and computing sigma proto ...

Association for Computing Machinery2019

ClaimChain: Improving the Security and Privacy of In-band Key Distribution for Messaging

Carmela González Troncoso, Wouter Lueks, Bogdan Kulynych

The social demand for email end-to-end encryption is barely supported by mainstream service providers. Autocrypt is a new community -driven open specification for e-mail encryption that attempts to respond to this demand. In Autocrypt the encryption keys a ...

ACM2018