TY - GEN

T1 - Universal Randomized Guessing Subject to Distortion

AU - Cohen, Asaf

AU - Merhav, Neri

N1 - Publisher Copyright:
© 2022 IEEE.

PY - 2022/1/1

Y1 - 2022/1/1

N2 - Consider the problem of guessing a sequence subject to a distortion constraint. Specifically, assume the following game between Alice and Bob: Alice has a sequence x of length n. Bob wishes to guess x, yet he is satisfied with finding any sequence x which is within a given distortion D from x. Thus, he successively submits queries to Alice, until receiving an affirmative answer, stating that his guess was within the required distortion. Finding guessing strategies which minimize the number of guesses and analyzing its properties has applications in information security, source and channel coding. Guessing subject to a distortion constraint is especially useful when considering biometrically-secured systems, where the "password"which protects the data is not a single, fixed vector but rather a ball of feature vectors centered at some x, and any feature vector within the ball results in acceptance. We formally define the guessing problem under distortion in four different setups: memoryless sources, guessing through a noisy channel, sources with memory, and individual sequences. We suggest a randomized guessing strategy which is asymptotically optimal for all setups and is five-fold universal, as it is independent of the source statistics, the channel, the moment to be optimized, the distortion measure and the distortion level.

AB - Consider the problem of guessing a sequence subject to a distortion constraint. Specifically, assume the following game between Alice and Bob: Alice has a sequence x of length n. Bob wishes to guess x, yet he is satisfied with finding any sequence x which is within a given distortion D from x. Thus, he successively submits queries to Alice, until receiving an affirmative answer, stating that his guess was within the required distortion. Finding guessing strategies which minimize the number of guesses and analyzing its properties has applications in information security, source and channel coding. Guessing subject to a distortion constraint is especially useful when considering biometrically-secured systems, where the "password"which protects the data is not a single, fixed vector but rather a ball of feature vectors centered at some x, and any feature vector within the ball results in acceptance. We formally define the guessing problem under distortion in four different setups: memoryless sources, guessing through a noisy channel, sources with memory, and individual sequences. We suggest a randomized guessing strategy which is asymptotically optimal for all setups and is five-fold universal, as it is independent of the source statistics, the channel, the moment to be optimized, the distortion measure and the distortion level.

UR - http://www.scopus.com/inward/record.url?scp=85136249252&partnerID=8YFLogxK

U2 - 10.1109/ISIT50566.2022.9834719

DO - 10.1109/ISIT50566.2022.9834719

M3 - Conference contribution

AN - SCOPUS:85136249252

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 3345

EP - 3350

BT - 2022 IEEE International Symposium on Information Theory, ISIT 2022

PB - Institute of Electrical and Electronics Engineers

T2 - 2022 IEEE International Symposium on Information Theory, ISIT 2022

Y2 - 26 June 2022 through 1 July 2022

ER -