Secure sound classification: Gaussian mixture models

Madhusudana V.S. Shashanka, Paris Smaragdis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose secure protocols for gaussian mixture-based sound recognition. The protocols we describe allow varying levels of security between two collaborating parties. The case we examine consists of one party (Alice) providing data and other party (Bob) providing a recognition algorithm. We show that it is possible to have Bob apply his algorithm on Alice's data in such a way that the data and the recognition results will not be revealed to Bob thereby guaranteeing Alice's data privacy. Likewise we show that it is possible to organize the collaboration so that a reverse engineering of Bob's recognition algorithm cannot be performed by Alice. We show how gaussian mixtures can be implemented in a secure manner using secure computation primitives implementing simple numerical operations and we demonstrate the process by showing how it can yield identical results to a non-secure computation while maintaining privacy.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
StatePublished - Dec 1 2006
Externally publishedYes
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: May 14 2006May 19 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
ISSN (Print)1520-6149

Other

Other2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
Country/TerritoryFrance
CityToulouse
Period5/14/065/19/06

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Secure sound classification: Gaussian mixture models'. Together they form a unique fingerprint.

Cite this