Compressive list-support recovery for colluder identification

Author

Pham, Hoa Vinh ; Dai, Wei ; Milenkovic, Olgica

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA

fYear

2010

fDate

14-19 March 2010

Firstpage

4166

Lastpage

4169

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.

Keywords

data compression; decoding; fingerprint identification; optimisation; colluder identification; compressive list-support recovery; compressive sensing approaches; digital fingerprinting systems; partial colluder identification; single-step correlation maximization techniques; Digital signal processing; Fingerprint recognition; Matching pursuit algorithms; Noise measurement; Polynomials; Pursuit algorithms; Signal processing; Signal sampling; Testing; Vectors; Compressive sensing; digital fingerprinting; statistical signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on

Conference_Location

Dallas, TX

ISSN

1520-6149

Print_ISBN

978-1-4244-4295-9

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2010.5495706

Filename

5495706