51 citations · 148 across the 13 of their papers we have counts for
14 papers
Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
Thomas Schneider, Ajith Suresh, Hossein Yalame
In August 2021, Liu et al. (IEEE TIFS'21) proposed a privacy-enhanced framework named PEFL to efficiently detect poisoning behaviours in Federated Learning (FL) using homomorphic e…
Attesting Distributional Properties of Training Data for Machine Learning
Vasisht Duddu, Anudeep Das, Nora Khayata +3
The success of machine learning (ML) has been accompanied by increased concerns about its trustworthiness. Several jurisdictions are preparing ML regulatory frameworks. One such co…
ExTRUST: Reducing Exploit Stockpiles with a Privacy-Preserving Depletion System for Inter-State Relationships
Thomas Reinhold, Philipp Kuehn, Daniel Günther +2
Cyberspace is a fragile construct threatened by malicious cyber operations of different actors, with vulnerabilities in IT hardware and software forming the basis for such activiti…
WW-FL: Secure and Private Large-Scale Federated Learning
Felix Marx, Thomas Schneider, Ajith Suresh +3
Federated learning (FL) is an efficient approach for large-scale distributed machine learning that promises data privacy by keeping training data on client devices. However, recent…
ScionFL: Efficient and Robust Secure Quantized Aggregation
Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas +7
Secure aggregation is commonly used in federated learning (FL) to alleviate privacy concerns related to the central aggregator seeing all parameter updates in the clear. Unfortunat…
Privacy-Preserving Epidemiological Modeling on Mobile Graphs
Daniel Günther, Marco Holz, Benjamin Judkewitz +4
The latest pandemic COVID-19 brought governments worldwide to use various containment measures to control its spread, such as contact tracing, social distance regulations, and curf…
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…
FLAME: Taming Backdoors in Federated Learning (Extended Version 1)
Thien Duc Nguyen, Phillip Rieger, Huili Chen +10
Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive loc…
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…
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…