2 research outputs found
A Generative Framework for Low-Cost Result Validation of Outsourced Machine Learning Tasks
The growing popularity of Machine Learning (ML) has led to its deployment in
various sensitive domains, which has resulted in significant research focused
on ML security and privacy. However, in some applications, such as autonomous
driving, integrity verification of the outsourced ML workload is more
critical--a facet that has not received much attention. Existing solutions,
such as multi-party computation and proof-based systems, impose significant
computation overhead, which makes them unfit for real-time applications. We
propose Fides, a novel framework for real-time validation of outsourced ML
workloads. Fides features a novel and efficient distillation technique--Greedy
Distillation Transfer Learning--that dynamically distills and fine-tunes a
space and compute-efficient verification model for verifying the corresponding
service model while running inside a trusted execution environment. Fides
features a client-side attack detection model that uses statistical analysis
and divergence measurements to identify, with a high likelihood, if the service
model is under attack. Fides also offers a re-classification functionality that
predicts the original class whenever an attack is identified. We devised a
generative adversarial network framework for training the attack detection and
re-classification models. The evaluation shows that Fides achieves an accuracy
of up to 98% for attack detection and 94% for re-classification.Comment: 16 pages, 11 figure
Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-Ï auto-Abs in children
We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2