Person
Bogdan Kulynych
This person is no longer with EPFL
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Related publications (3)
Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.
Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning
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 ...
EPFL2023Arbitrary 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 ...
New York2023ClaimChain: Improving the Security and Privacy of In-band Key Distribution for Messaging
Carmela González Troncoso, Bogdan Kulynych, Wouter Lueks
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