Title
Reduced Relative Errors for Short Sequence Counting with Differential Privacy
Abstract
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.
Year
DOI
Venue
2015
10.1109/CSCS.2015.83
2015 20th International Conference on Control Systems and Computer Science
Keywords
Field
DocType
Differential privacy,Sequence counting,Optimization,Privacy
Resource management,Data mining,Differential privacy,Computer science,Data anonymization,Complex data type,Data type,Information privacy,Privacy software,Recursion
Conference
ISSN
Citations 
PageRank 
2379-0474
1
0.37
References 
Authors
12
4
Name
Order
Citations
PageRank
Sergiu Costea132.78
Gabriel Ghinita2196487.44
Razvan Rughinis32513.70
Nicolae Tapus48434.55