Research map / Federated learning and privacy / Differential privacy
Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| perturb polynomial-approximated objective functions (dPAH) vs differentially private regression methods FM, FP, DPME | human behavior prediction · Health social network (daily and weekly) | prediction accuracy | Helped |
| enforce differential privacy (dPAH vs non-private dA/TdA) | deep auto-encoder · Health social network | prediction accuracy | Hurt |
| dPAH vs non-private SctRBM at sampling rate > 0.3 | human behavior prediction · Health social network | prediction accuracy | Mixed |
| decrease privacy budget epsilon | dPAH · Health social network | prediction accuracy | No clear effect |
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