QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

Table 7

Precision and recall of noisy dataset in different groups.

Group			Group 1	Group 2	Group 3	Group 4	Group 5
Tree-based	J48	Precision	0.68	0.71	0.75	0.72	0.72
		Recall	0.37	0.40	0.44	0.36	0.43
	LMT	Precision	0.69	0.70	0.70	0.75	0.72
		Recall	0.36	0.38	0.43	0.33	0.42
Rule-based	Decision Table	Precision	0.86	0.89	0.90	0.79	0.79
		Recall	0.33	0.32	0.41	0.34	0.41
	JRip	Precision	0.85	0.80	0.85	0.77	0.78
		Recall	0.25	0.23	0.32	0.23	0.34
	OneR	Precision	0.46	0.39	0.48	0.46	0.51
		Recall	0.21	0.17	0.27	0.25	0.35
Lazy learner	IBK	Precision	0.74	0.78	0.83	0.78	0.77
		Recall	0.42	0.44	0.48	0.38	0.45
	KStar	Precision	0.75	0.78	0.77	0.76	0.72
		Recall	0.36	0.40	0.44	0.35	0.72
Metaheuristic	Bagging	Precision	0.77	0.74	0.78	0.79	0.73
		Recall	0.37	0.41	0.45	0.36	0.44
	LogitBoost	Precision	0.50	0.29	0.28	0.37	0.36
		Recall	0.12	0.10	0.17	0.12	0.30
Bayesian	Bayes Net	Precision	0.77	0.71	0.77	0.78	0.69
		Recall	0.32	0.36	0.42	0.33	0.44