InfoSec Seminar: Efficient Private Statistics with Succinct Sketches

Speaker: Luca Melis

Date/Time: 18-Feb-2016, 16:00 UTC




Large-scale collection of contextual information is often essential in order to gather statistics, train machine learning models, and extract knowledge from data. The ability to do so in a privacy-preserving way -- i.e., without collecting fine-grained user data -- enables a number of additional computational scenarios that would be hard, or outright impossible, to realize without strong privacy guarantees. In this paper, we present the design and implementation of practical techniques for privately gathering statistics from large data streams. We build on efficient cryptographic protocols for private aggregation and on data structures for succinct data representation, namely, Count-Min Sketch and Count Sketch. These allow us to reduce the communication and computation complexity incurred by each data source (e.g., end-users) from linear to logarithmic in the size of their input, while introducing a parametrized upper-bounded error that does not compromise the quality of the statistics. We then show how to use our techniques, efficiently, to instantiate real-world privacy-friendly systems, supporting recommendations for media streaming services, prediction of user locations, and computation of median statistics for Tor hidden services.

To appear at NDSS16 and available from here.



Is currently a Ph.D. student in the Information Security Group of the Computer Science department at University College London.
He works under the supervision of Dr. Emiliano De Cristofaro, and collaborate with Dr. George Danezis.

His research interests are in the fields of applied cryptography, privacy and cloud security.

Before joining UCL, he received a BSc. and a MSc. in Computer Engineering from the University of Florence.

This page was last modified on 27 Mar 2014.