Reduced Relative Errors for Short Sequence Counting with Differential Privacy

Current concerns about data privacy have lead to increased focus on data anonymization methods. Differential privacy is a new mechanism that offers formal guarantees about anonymization strength. The main challenge when using differential privacy consists in the difficulty in designing correct algorithms when operating on complex data types. One such data type is sequential data, which is used to model many actions like location or browsing history. We propose a new differential privacy algorithm for short sequence counting called Recursive Budget Allocation (RBA). We show that RBA leads to lower relative errors than current state of the art techniques. In addition, it can also be used to improve relative errors for generic differential privacy algorithms which operate on data trees.