The invention discloses an individual
differential privacy protection method for high-dimensional
data publishing in a distributed environment. According to the method, the correlation among properties is quantified through
mutual information, and the
mutual information of corresponding property pairs is calculated by use of a
mutual information formula; an approximate k-degree
Bayesian network isconstructed according to the mutual information, and the
Bayesian network can well reflect dependency among the properties; privacy budgets are allocated individually according to the quantity of sensitive properties and the quantity of non-sensitive properties meeting conditions; all participants perform
noise addition
processing on data according to the allocated privacy budgets, and a
random response mechanism is adopted to perform
noise addition; and the data obtained after
noise addition is sent to a manager, the manager gathers the data and synthesizes the data into an integrated dataset, and then the dataset is published to the outside. Through the method, when the data is published, a privacy requirement is guaranteed, a large amount of
processing data is reduced, therefore, change of the data is lowered, the utility of the data is improved, and the method is beneficial for a data analyzer to perform relevant analysis.