Guessing Individual Sequences: Generating Randomized Guesses Using Finite-State Machines

Motivated by earlier results on universal randomized guessing, we consider an individual-sequence approach to the guessing problem: in this setting, the goal is to guess a secret, individual (deterministic) vector <inline-formula> <tex-math notation="LaTeX">x^{n}=(x_{1},\ldots,...

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Veröffentlicht in:IEEE transactions on information theory Jg. 66; H. 5; S. 2912 - 2920
1. Verfasser: Merhav, Neri
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Asymptotic properties
Binary trees
Compressibility
Data compression
Decoding
finite–state machine
Guessing exponent
Hidden Markov models
incremental parsing
individual sequences
Lempel–Ziv algorithm
Password
Probabilistic logic
Randomization
randomized guessing
sequence complexity
side information
State machines
Viterbi algorithm
ISSN:0018-9448, 1557-9654
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Zusammenfassung:Motivated by earlier results on universal randomized guessing, we consider an individual-sequence approach to the guessing problem: in this setting, the goal is to guess a secret, individual (deterministic) vector <inline-formula> <tex-math notation="LaTeX">x^{n}=(x_{1},\ldots,x_{n}) </tex-math></inline-formula>, by using a finite-state machine that sequentially generates randomized guesses from a stream of purely random bits. We define the finite-state guessing exponent as the asymptotic normalized logarithm of the minimum achievable moment of the number of randomized guesses, generated by any finite-state machine, until <inline-formula> <tex-math notation="LaTeX">x^{n} </tex-math></inline-formula> is guessed successfully. We show that the finite-state guessing exponent of any sequence is intimately related to its finite-state compressibility (due to Lempel and Ziv), and it is asymptotically achieved by the decoder of (a certain modified version of) the 1978 Lempel-Ziv data compression algorithm (a.k.a. the LZ78 algorithm), fed by purely random bits. The results are also extended to the case where the guessing machine has access to a side information sequence, <inline-formula> <tex-math notation="LaTeX">y^{n}=(y_{1},\ldots,y_{n}) </tex-math></inline-formula>, which is also an individual sequence.
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ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2019.2946303