Title:

When decentralization, security, and privacy are not friends

 

Abstract:

Decentralization is often seen as a main tool to achieve security and privacy. It has worked in a number of systems, for which decentralization help protect identities and data of users. Thus, it is not a surprise that a new trend of machine learning algorithms opt for decentralization to increase data privacy. In this talk, we will analyze decentralized machine learning proposals and show how they not only don't improve privacy or robustness, but also increase the surface of attack resulting in less protection than federated alternatives.

 

Bio:

Carmela Troncoso is an Associate Professor at EPFL (Switzerland) where she heads the SPRING Lab. Her work focuses on analyzing, building, and deploying secure and privacy-preserving systems. Troncoso holds a Ph.D. in engineering from KULeuven. Her thesis, Design and Analysis Methods for Privacy Technologies, received the European Research Consortium for Informatics and Mathematics Security and Trust Management Best Ph.D. Thesis Award, and her work on privacy engineering received the CNIL-INRIA Privacy Protection Award in 2017. She has been named 40 under 40 in technology by Fortune in 2020.

 

Speaker:

Prof. Carmela Troncoso

SPRING Lab
EPFL (Switzerland)