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Found 7 papers, 4 papers with code
Packing Privacy Budget Efficiently
This DP budget can be regarded as a new type of compute resource in workloads of multiple ML models training on user data.
How to Combine Membership-Inference Attacks on Multiple Updated Models
Our results on four public datasets show that our attacks are effective at using update information to give the adversary a significant advantage over attacks on standalone models, but also compared to a prior MI attack that takes advantage of model updates in a related machine-unlearning setting.
Toward Training at ImageNet Scale with Differential Privacy
Despite a rich literature on how to train ML models with differential privacy, it remains extremely challenging to train real-life, large neural networks with both reasonable accuracy and privacy.
Privacy Budget Scheduling
We describe PrivateKube, an extension to the popular Kubernetes datacenter orchestrator that adds privacy as a new type of resource to be managed alongside other traditional compute resources, such as CPU, GPU, and memory.
Pythia: Grammar-Based Fuzzing of REST APIs with Coverage-guided Feedback and Learning-based Mutations
This paper introduces Pythia, the first fuzzer that augments grammar-based fuzzing with coverage-guided feedback and a learning-based mutation strategy for stateful REST API fuzzing.
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform
Companies increasingly expose machine learning (ML) models trained over sensitive user data to untrusted domains, such as end-user devices and wide-access model stores.
Certified Robustness to Adversarial Examples with Differential Privacy
Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed in a tight back-and-forth.
