A new probing framework detects moderate parametric memorization signals in tabular in-context learning models under single-task fine-tuning, strongest on low-cardinality tasks, but signals largely disappear under realistic training.
Sok: machine learning with confidential computing
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DIST-FL distributes TEE-guarded servers into an append-only ledger to ensure linearizable FL aggregation and counter rollback plus I/O attacks while matching single-TEE speed.
Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.
citing papers explorer
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Probing Memorization of Tabular In-Context Learning
A new probing framework detects moderate parametric memorization signals in tabular in-context learning models under single-task fine-tuning, strongest on low-cardinality tasks, but signals largely disappear under realistic training.
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DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning
DIST-FL distributes TEE-guarded servers into an append-only ledger to ensure linearizable FL aggregation and counter rollback plus I/O attacks while matching single-TEE speed.
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Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.