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

Algorithm 1

Associating incoming query to the user using the prior profile.

	Input: User Profile (PU); all session windows belong to the user ().
	Output: Expected User Label (Lu)
(1)	procedure QUERY ASSOCIATION (PU, )
(2)	fordo
(3)
(4)
(5)	fordo
(6)	fordo
(7)
(8)
(9)	return
(1)	Firstly, the user profile (PU) feature vector is acquired for training purposes. The user profile with the feature vector () is shown in equation (4). The feature vector is acquired from the uClassify (http://www.uclassify.com) service, a machine learning web service that provides numerous different classifiers for text classification. We have selected the “Topics” classifier that gives the score of each phrase or query in 10 major classes including Sports, Society, Science, Recreation, Home, Health, Games, Computers, Business, and Arts.
(2)	In the second step, a classification model PModel is built using and supervised machine learning algorithms. To test the response of the data with different classification techniques, 10 classification algorithms are selected from tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families.
(3)	After the classification model (PModel), the third step is to acquire the feature vector shown in equation (5) for the queries of session window Swin from uClassify for testing data.
(4)	In the last step, each query of is provided to the classification model for the expected label Lu. The label Lu shows whether the incoming query belongs to UoI or not.