Searched for: subject:"Multi%5C-Party%5C+Computation"
(1 - 10 of 10)
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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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Attema, T. (author), Worm, D. (author)
challenges.
report 2021
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van Haaften, W. (author), Sangers, A. (author), Engers, T. (author), Djafari, s. (author)
Analysing combined data sets can result in signifi cant added value for many organisations, but the GDPR has put strict constraints on processing personal data. Anonymization by using Multi-Party Computation (MPC) however may off er organizations some relief of the perceived burden of GDPR under specifi c conditions. In this paper, we will...
conference paper 2020
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Spini, G. (author), van Heesch, M.P.P. (author), Veugen, P.J.M. (author), Chatterjea, S. (author)
Optimizing the workflow of a complex organization such as a hospital is a difficult task. An accurate option is to use a real-time locating system to track locations of both patients and staff. However, privacy regulations forbid hospital management to assess location data of their staff members. In this exploratory work, we propose a secure...
article 2019
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Veeningen, M. (author), Chatterjea, S. (author), Horváth, A.Z. (author), Spindler, G. (author), Boersma, E. (author), van der Spek, P. (author), van der GaliËn, O. (author), Gutteling, J. (author), Kraaij, W. (author), Veugen, P.J.M. (author)
conference paper 2018
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Veugen, P.J.M. (author), Blom, F. (author), de Hoogh, S.J.A. (author), Erkin, Z. (author)
Due to high complexity, comparison protocols with secret inputs have been a bottleneck in the design of privacy-preserving cryptographic protocols. Different solutions based on homomorphic encryption, garbled circuits and secret sharing techniques have been proposed over the last few years, each claiming high efficiency. Unfortunately, a fair...
article 2015
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Veugen, P.J.M. (author)
In the field of signal processing in the encrypted domain, linear operations are usually easy to perform, whereas multiplications, and bitwise operations like comparison, are more costly in terms of computation and communication. These bitwise operations frequently require a decomposition of the secret value into bits. To minimize the...
article 2015
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Veugen, P.J.M. (author)
When processing data in the encrypted domain, homomorphic encryption can be used to enable linear operations on encrypted data. Integer division of encrypted data however requires an additional protocol between the client and the server and will be relatively expensive. We present new solutions for dividing encrypted data in the semi-honest...
article 2014
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Makri, E. (author), Everts, M.H. (author), de Hoogh, S. (author), Peter, A. (author), op den Akker, H. (author), Hartel, P.H. (author), Jonker, W. (author)
We treat the problem of privacy-preserving statistics verification in clinical research. We show that given aggregated results from statistical calculations, we can verify their correctness efficiently, without revealing any of the private inputs used for the calculation. Our construction is based on the primitive of Secure Multi-Party...
conference paper 2014
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Erkin, Z. (author), Veugen, P.J.M. (author), Lagendijk, R.L. (author)
Recommender systems have become increasingly important in e-commerce as they can guide customers with finding personalized services and products. A variant of recommender systems that generates recommendations from a set of trusted people is recently getting more attention in social networks. However, people are concerned about their privacy as...
conference paper 2011
Searched for: subject:"Multi%5C-Party%5C+Computation"
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