A privacy preserving measurement for query under k-anonymity mechanism

A query privacy measurement was proposed under the k-anonymity mechanism. The method was based on information entropy and logarithmic function. First, a framework for query privacy under the k-anonymity mechanism is established, which contains four roles and four operations provides a formal description for privacy measurement. Then, two quantitative methods of background knowledge are introduced. For the second step, user attribute discretization values will be calculated as a probability expression of background knowledge, affects the accuracy of the probability expression. The value of each user attribute after discretization was proposed as the index of the array to calculate the relevant quantities, the index of the array being generated by the relevance of the particular query and the attributes of the user, so as to further obtain the probability of the user issuing the particular query, thus avoiding the influence of discretized values of user attributes on the quantification results. Finally, a query privacy measurement is proposed. The experimental results show that the method can effectively measure the level of protection of the query privacy protection algorithm under k-anonymity mechanism.