Sparse discrete-Laplace and Gaussian local DP mechanisms admit exact privacy characterizations, with support cardinality as the key parameter that sets a minimum size for nontrivial approximate privacy and yields an optimal smallest-support design rule.
The algorithmic foundations of differential privacy
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
PAS encodes locations via relative anchors and bins to deliver roughly 370-400m adversarial error in spatial RAG while retaining over half the baseline retrieval performance and keeping generation quality robust.
Generative models for trajectory data do not inherently preserve privacy, as membership inference attacks can identify training data points in representative models.
citing papers explorer
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Sparse Discrete Laplace and Gaussian Mechanisms under Local Differential Privacy
Sparse discrete-Laplace and Gaussian local DP mechanisms admit exact privacy characterizations, with support cardinality as the key parameter that sets a minimum size for nontrivial approximate privacy and yields an optimal smallest-support design rule.
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Privacy Without Losing Place: A Paradigm for Private Retrieval in Spatial RAGs
PAS encodes locations via relative anchors and bins to deliver roughly 370-400m adversarial error in spatial RAG while retaining over half the baseline retrieval performance and keeping generation quality robust.
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Privacy Evaluation of Generative Models for Trajectory Generation
Generative models for trajectory data do not inherently preserve privacy, as membership inference attacks can identify training data points in representative models.