Universal Randomized Guessing Subject to Distortion.

Asaf Cohen, Neri Merhav

Research output: Contribution to conferencePaperpeer-review


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.
Original languageEnglish
Number of pages6
StatePublished - 3 Aug 2022
Event2022 IEEE International Symposium on Information Theory, ISIT 2022 - Espoo, Finland
Duration: 26 Jun 20221 Jul 2022


Conference2022 IEEE International Symposium on Information Theory, ISIT 2022


  • Biometrics (access control)
  • Information security
  • Passwords
  • Games
  • Distortion
  • Noise measurement
  • Distortion measurement

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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