The tradeoff between storage and identification rates for multiple databases is investigated from an information theoretic perspective. In the assumed model, noisy observations of feature vectors of two distinct groups, called the ancestors, are compressed and stored in two separate databases. When queried with a noisy observation of a (possibly random) function of two randomly selected ancestors (one from each group), the system is required to correctly identify the ancestors with high probability. Single-letter inner and outer bounds are presented on the set of achievable rate points, which identify a tradeoff between the compression rates and the identification rate region: the lower the compression rates for storage, the larger the rate region achievable for identification. © 2009 IEEE.
Identification over multiple databases / Gunduz, D.; Tuncel, E.; Goldsmith, A.; Poor, V.. - (2009), pp. 2311-2315. (Intervento presentato al convegno 2009 IEEE International Symposium on Information Theory, ISIT 2009 tenutosi a Seoul, kor nel 2009) [10.1109/ISIT.2009.5205930].
Identification over multiple databases
D. Gunduz;
2009
Abstract
The tradeoff between storage and identification rates for multiple databases is investigated from an information theoretic perspective. In the assumed model, noisy observations of feature vectors of two distinct groups, called the ancestors, are compressed and stored in two separate databases. When queried with a noisy observation of a (possibly random) function of two randomly selected ancestors (one from each group), the system is required to correctly identify the ancestors with high probability. Single-letter inner and outer bounds are presented on the set of achievable rate points, which identify a tradeoff between the compression rates and the identification rate region: the lower the compression rates for storage, the larger the rate region achievable for identification. © 2009 IEEE.
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