Compressive list-support recovery for colluder identification

Hoa Vinh Pham, Wei Dai, Olgica Milenkovic

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

Abstract

One of the main computational challenges in digital fingerprinting systems is the complexity of colluder identification. Inspired by compressive sensing approaches for support recovery of sparse vectors, we propose a novel list-decoding approach for partial colluder identification. We also derive formulas for the minimum codelength required for identifying a nonzero fraction of colluders based on noiseless and noisy measurements, using simple single-step correlation maximization techniques.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages4166-4169
Number of pages4
DOIs
StatePublished - Nov 8 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

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

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • Compressive sensing
  • Digital fingerprinting
  • Statistical signal processing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Pham, H. V., Dai, W., & Milenkovic, O. (2010). Compressive list-support recovery for colluder identification. In 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings (pp. 4166-4169). [05495706] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2010.5495706