9 citations · 19 across the 3 of their papers we have counts for
4 papers
Trustworthy AI Inference Systems: An Industry Research View
Rosario Cammarota, Matthias Schunter, Anand Rajan +26
In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems…
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…