Research map / Optimization / Nonconvex and smooth optimization
A Smooth Binary Mechanism for Efficient Private Continual Observation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use only balanced-binary-representation leaves (smooth binary mechanism) instead of all leaves | Binary mechanism · Continual counting | normalized variance: $1$ -> $0.25 + o(1)$ | Helped |
| Smooth binary vs binary mechanism at T=250 last step | Binary mechanism · Continual counting | variance: 3.27 | Helped |
| Use smooth binary mechanism with stable error | Binary mechanism · Continual counting | error distribution across time | Helped |
| Use matrix factorization mechanism instead of smooth binary | Smooth Binary Mechanism · Continual counting | variance: $0.25 + o(1)$ -> $0.0487\hdots + o(1)$ | Helped |
| Use smooth binary mechanism instead of factorization mechanism | Factorization mechanism · Continual counting, d=10^4 | time and space: $O(T^2)$ -> $O(\log{T})$ | Helped |
| Smooth binary vs binary mechanism noise generation | Binary mechanism · Continual counting | computation time | Hurt |
| Honaker Online vs binary mechanism | Binary mechanism · Continual counting | variance: $1$ -> $0.5 + o(1)$ | Helped |
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