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Veugen, P.J.M. (author), Kamphorst, B (author), van de L'Isle, N. (author), van Egmond, M.B. (author)
We show how multiple data-owning parties can collabora tively train several machine learning algorithms without jeopardizing the privacy of their sensitive data. In particular, we assume that every party knows specific features of an overlapping set of people. Using a secure implementation of an advanced hidden set intersection protocol and a...
conference paper 2021
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Phillipson, F. (author), Chiscop, I. (author)
conference paper 2021
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Neumann, N.M.P. (author)
conference paper 2021
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Marko, N. (author), Vasenev, A. (author), Striecks, C. (author)
In the past several years, autonomous driving turned out tobe a target for many technical players. Automated driving requires newand advanced mechanisms to provide safe functionality and the increased communication makes automated vehicles more vulnerable to attacks. Security is already well-established in some domains, such as the IT sector,...
conference paper 2020
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Diran, D.D.D. (author), van Veenstra, A.F.E. (author)
conference paper 2020
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Gilsing, R. (author), Turetken, O. (author), Ozkan, B. (author), Slaats, F. (author), Adali, O.E. (author), Wilbik, A. (author), Berkers, F.T.H.M (author), Grefen, P. (author)
conference paper 2020
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Berkers, F.T.H.M. (author), Turetken, O. (author), Ozkan, B. (author), Wilbik, A. (author), Adali, O.E. (author), Gilsing, R. (author), Grefen, P. (author)
conference paper 2020
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van Engelenburg, S. (author), Rukanova, B. (author), Hofman, W.J. (author), Ubacht, J. (author), Tan, Y.H. (author), Janssen, M. (author)
conference paper 2020
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