Publications by Bogdan Kulynych

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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 ...

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 ...

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 ...

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 ...

Bogdan Kulynych

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

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

zksk: A Library for Composable Zero-Knowledge Proofs

POTs: Protective Optimization Technologies