Some examples of privacy-preserving sharing of COVID-19 pandemic data with statistical utility evaluation

Table 3 Privacy-preserving inferences of Matérn cluster point process model on simulated location data (1,000 repeats; \(m=3\))

Metric	Parameter	Original	\(\epsilon =5\)	\(\epsilon =2\)	\(\epsilon =1\)	\(\epsilon =0.5\)
	\(\beta _0\)	-0.029	-0.022	0.016	0.142	0.571
	\(\beta _1\)	0.065	0.052	-0.022	-0.279	-1.180
bias	\(\beta _2\)	0.031	0.014	-0.074	-0.374	-1.389
	\(\beta _3\)	-0.085	-0.077	-0.028	0.154	0.801
	\(\beta _4\)	0.034	0.038	0.060	0.124	0.337
	\(\beta _5\)	-0.037	-0.024	0.048	0.303	1.160
	\(\beta _0\)	0.466	0.465	0.459	0.457	0.680
	\(\beta _1\)	1.234	1.232	1.211	1.189	1.549
RMSE	\(\beta _2\)	1.164	1.162	1.152	1.166	1.693
	\(\beta _3\)	1.006	1.003	0.986	0.958	1.159
	\(\beta _4\)	0.944	0.943	0.934	0.898	0.838
	\(\beta _5\)	0.985	0.982	0.972	0.989	1.431
	\(\beta _0\)	0.948	0.940	0.925	0.841	0.599
	\(\beta _1\)	0.938	0.932	0.914	0.845	0.719
CP	\(\beta _2\)	0.957	0.952	0.935	0.851	0.640
	\(\beta _3\)	0.938	0.929	0.909	0.842	0.769
	\(\beta _4\)	0.941	0.934	0.908	0.840	0.878
	\(\beta _5\)	0.947	0.939	0.916	0.827	0.638

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