CSLR aligns unordered private replay lists from clients using public anchor sentence signatures, yielding 3.9-5.6 point gains on continual NLP tasks at ε=4 over non-CSLR DP baselines.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Shuffled DP-SGD requires σ ≥ 1/√(2 ln M) or κ ≥ (1/√8)(1 - 1/√(4π ln M)) to limit adversarial advantage, preventing strong privacy and high utility simultaneously.
InvisibleInk achieves high-utility differentially private long-form LLM text generation at 4-8x the cost of non-private generation by isolating and clipping sensitive logits and sampling from a small superset of top-k private tokens without privacy cost.
citing papers explorer
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Canonicalized Stable-List Replay for Private Federated Continual Learning over Language-Model Embeddings
CSLR aligns unordered private replay lists from clients using public anchor sentence signatures, yielding 3.9-5.6 point gains on continual NLP tasks at ε=4 over non-CSLR DP baselines.
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Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
Shuffled DP-SGD requires σ ≥ 1/√(2 ln M) or κ ≥ (1/√8)(1 - 1/√(4π ln M)) to limit adversarial advantage, preventing strong privacy and high utility simultaneously.
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InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy
InvisibleInk achieves high-utility differentially private long-form LLM text generation at 4-8x the cost of non-private generation by isolating and clipping sensitive logits and sampling from a small superset of top-k private tokens without privacy cost.