Abstract：Most existing methods for differential privacy streaming data statistical release based on tree structures cannot take advantage of the special probability distributions in statistical queries. This study presents an algorithm that further boosts the accuracy of the released streaming data for differential privacy streaming data publication with non-uniform private budgets using sliding windows. After constructing the differential privacy range tree for the streaming data within the sliding window, the algorithm calculates the coverage probability of the tree nodes according to the probability distribution of the statistical queries and then adds non-uniform noise to the tree nodes based on the adjusted privacy budget of the tree nodes and a tree structure parameter. Finally, several real-time adjustment policies are designed to ensure that the node values on any path from the root to the leaves satisfy a consistency constraint. Tests show that the algorithm guarantees better statistical query accuracy and has higher algorithm efficiency than traditional algorithms.
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2019, Vol. 59 
