51 citations · 92 across the 4 of their papers we have counts for
6 papers
SPIKE: Secure and Private Investigation of the Kidney Exchange problem
Timm Birka, Kay Hamacher, Tobias Kussel +2
Background: The kidney exchange problem (KEP) addresses the matching of patients in need for a replacement organ with compatible living donors. Ideally many medical institutions sh…
Offline Model Guard: Secure and Private ML on Mobile Devices
Sebastian P. Bayerl, Tommaso Frassetto, Patrick Jauernig +5
Performing machine learning tasks in mobile applications yields a challenging conflict of interest: highly sensitive client information (e.g., speech data) should remain private wh…
CryptoSPN: Privacy-preserving Sum-Product Network Inference
Amos Treiber, Alejandro Molina, Christian Weinert +2
AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e.g.,…
Privacy-Preserving Speaker Recognition with Cohort Score Normalisation
Andreas Nautsch, Jose Patino, Amos Treiber +5
In many voice biometrics applications there is a requirement to preserve privacy, not least because of the recently enforced General Data Protection Regulation (GDPR). Though progr…
A Comment on Privacy-Preserving Scalar Product Protocols as proposed in "SPOC"
Thomas Schneider, Amos Treiber
Privacy-preserving scalar product (PPSP) protocols are an important building block for secure computation tasks in various applications. Lu et al. (TPDS'13) introduced a PPSP proto…
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
M. Sadegh Riazi, Christian Weinert, Oleksandr Tkachenko +3
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing thei…