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This paper provides upper and lower bounds on the optimal guessing moments of a random variable taking values on a finite set when side information may be available. These moments quantify the number of guesses required for correctly…

Information Theory · Computer Science 2018-06-22 Igal Sason , Sergio Verdú

Compressed Counting (CC) [22] was recently proposed for estimating the ath frequency moments of data streams, where 0 < a <= 2. CC can be used for estimating Shannon entropy, which can be approximated by certain functions of the ath…

Data Structures and Algorithms · Computer Science 2012-05-14 Ping Li

Shannon's entropy is one of the building blocks of information theory and an essential aspect of Machine Learning methods (e.g., Random Forests). Yet, it is only finitely defined for distributions with fast decaying tails on a countable…

Statistics Theory · Mathematics 2022-05-25 Jialin Zhang , Jingyi Shi

The problem of Shannon entropy estimation in countable infinite alphabets is addressed from the study and use of convergence results of the entropy functional, which is known to be discontinuous with respect to the total variation distance…

Information Theory · Computer Science 2018-04-03 Jorge F. Silva

In this paper, new inner and outer bounds on the achievable compression-equivocation rate region for generalized secure data compression with side information are given that do not match in general. In this setup, two senders, Alice and…

Information Theory · Computer Science 2010-04-07 Somayeh Salimi , Mahmoud Salmasizadeh , Mohammad Reza Aref

We evaluate the asymptotics of equivocations, their exponents as well as their second-order coding rates under various R\'{e}nyi information measures. Specifically, we consider the effect of applying a hash function on a source and we…

Information Theory · Computer Science 2016-07-06 Masahito Hayashi , Vincent Y. F. Tan

Shannon entropy is the most crucial foundation of Information Theory, which has been proven to be effective in many fields such as communications. Renyi entropy and Chernoff information are other two popular measures of information with…

Information Theory · Computer Science 2017-01-13 Shanyun Liu , Rui She , Jiaxun Lu , Pingyi Fan

The Parity Source Coder is a protocol for data compression which is based on a set of parity checks organized in a sparse random network. We consider here the case of memoryless unbiased binary sources. We show that the theoretical capacity…

Disordered Systems and Neural Networks · Physics 2009-11-11 Stefano Ciliberti , Marc Mezard

Imperfect secrecy in communication systems is investigated. Instead of using equivocation as a measure of secrecy, the distortion that an eavesdropper incurs in producing an estimate of the source sequence is examined. The communication…

Information Theory · Computer Science 2012-10-09 Curt Schieler , Eva C. Song , Paul Cuff , H. Vincent Poor

In the first paper of this two part communication, we solved in a unified framework a variety of two terminal source coding problems with noncooperative encoders, thereby consolidating works of Shannon, Slepian-Wolf, Wyner,…

Information Theory · Computer Science 2007-07-13 Soumya Jana

Loosely speaking, the Shannon entropy rate is used to gauge a stochastic process' intrinsic randomness; the statistical complexity gives the cost of predicting the process. We calculate, for the first time, the entropy rate and statistical…

Statistical Mechanics · Physics 2017-09-13 S. E. Marzen , J. P. Crutchfield

Compressed Counting (CC), based on maximally skewed stable random projections, was recently proposed for estimating the p-th frequency moments of data streams. The case p->1 is extremely useful for estimating Shannon entropy of data…

Data Structures and Algorithms · Computer Science 2009-10-09 Ping Li

A lower bound on the secrecy capacity of the wiretap channel with state information available causally at both the encoder and decoder is established. The lower bound is shown to be strictly larger than that for the noncausal case by Liu…

Information Theory · Computer Science 2010-06-03 Yeow-Khiang Chia , Abbas El Gamal

We study an entropy measure for quantum systems that generalizes the von Neumann entropy as well as its classical counterpart, the Gibbs or Shannon entropy. The entropy measure is based on hypothesis testing and has an elegant formulation…

Quantum Physics · Physics 2014-02-19 F. Dupuis , L. Kraemer , P. Faist , J. M. Renes , R. Renner

We introduce a new protocol for a lossy data compression algorithm which is based on constraint satisfaction gates. We show that the theoretical capacity of algorithms built from standard parity-check gates converges exponentially fast to…

Disordered Systems and Neural Networks · Physics 2009-11-11 S. Ciliberti , M. Mezard , R. Zecchina

Shannon's perfect-secrecy theorem states that a perfect encryption system that yields zero information to the adversary must be a one-time pad (OTP) with the keys randomly generated and never reused. In this work we design the first…

Quantum Physics · Physics 2025-01-23 Zixuan Hu , Zhenyu Li

The secrecy of a distributed-storage system for passwords is studied. The encoder, Alice, observes a length-n password and describes it using two hints, which she stores in different locations. The legitimate receiver, Bob, observes both…

Information Theory · Computer Science 2017-01-10 Annina Bracher , Eran Hof , Amos Lapidoth

We leverage the Gibbs inequality and its natural generalization to R\'enyi entropies to derive closed-form parametric expressions of the optimal lower bounds of $\rho$th-order guessing entropy (guessing moment) of a secret taking values on…

Information Theory · Computer Science 2024-01-31 Julien Béguinot , Olivier Rioul

Upper and lower bounds are obtained for the joint entropy of a collection of random variables in terms of an arbitrary collection of subset joint entropies. These inequalities generalize Shannon's chain rule for entropy as well as…

Information Theory · Computer Science 2024-05-07 Mokshay Madiman , Prasad Tetali

The Shannon lower bound is one of the few lower bounds on the rate-distortion function that holds for a large class of sources. In this paper, it is demonstrated that its gap to the rate-distortion function vanishes as the allowed…

Information Theory · Computer Science 2016-11-17 Tobias Koch