A one-to-one correspondence maps maximal LDP channels under the Blackwell order to vertices of a finite-dimensional polytope, making optimal privacy-utility trade-offs computable via linear programming or vertex enumeration for general problems.
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QOP achieves (ε, δ)-differential privacy for ERM in the interpolation regime under weaker assumptions than linear objective perturbation by using random quadratic curvature to enforce stability and control sensitivity.
Differentially private synthetic data and seeded agent-based models can separate personal identities from usable financial data while meeting regulatory privacy rules.
citing papers explorer
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Optimal Privacy-Utility Trade-Offs in LDP: Functional and Geometric Perspectives
A one-to-one correspondence maps maximal LDP channels under the Blackwell order to vertices of a finite-dimensional polytope, making optimal privacy-utility trade-offs computable via linear programming or vertex enumeration for general problems.
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Quadratic Objective Perturbation: Curvature-Based Differential Privacy
QOP achieves (ε, δ)-differential privacy for ERM in the interpolation regime under weaker assumptions than linear objective perturbation by using random quadratic curvature to enforce stability and control sensitivity.
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Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems
Differentially private synthetic data and seeded agent-based models can separate personal identities from usable financial data while meeting regulatory privacy rules.