Tarik Kaced

Multivariate Mutual Information Inspired by Secret-Key Agreement

The capacity for multiterminal secret-key agreement inspires a natural generalization of Shannons mutual information from two random variables to multiple random variables. Under a general source model without helpers, the capacity is shown to be equal to the normalized divergence from the joint distribution of the random sources to the product of marginal distributions minimized over partitions of the random sources. The mathematical underpinnings are the works on co-intersecting submodular functions and the principle lattices of partitions of the Dilworth truncation. We clarify the connection to these works and enrich them with information-theoretic interpretations and properties that are useful in solving other related problems in information theory as well as machine learning.

Info-Clustering: A Mathematical Theory for Data Clustering

We formulate an info-clustering paradigm based on a multivariate information measure, called multivariate mutual information, that naturally extends Shannon’s mutual information between two random variables to the multivariate case involving more than two random variables. With proper model reductions, we show that the paradigm can be applied to study the human genome and connectome in a more meaningful way than the conventional algorithmic approach. Not only can info-clustering provide justifications and refinements to some existing techniques, but it also inspires new computationally feasible solutions.

Optimal Non-perfect Uniform Secret Sharing Schemes

A secret sharing scheme is non-perfect if some subsets of participants that cannot recover the secret value have partial information about it. The information ratio of a secret sharing scheme is the ratio between the maximum length of the shares and the length of the secret. This work is dedicated to the search of bounds on the information ratio of non-perfect secret sharing schemes. To this end, we extend the known connections between polymatroids and perfect secret sharing schemes to the non-perfect case.

Equivalence of two proof techniques for non-shannon-type inequalities

We compare two different techniques for proving non-Shannon-type information inequalities. The first one is the original Zhang-Yeungs method, commonly referred to as the copy/pasting lemma/trick. The copy lemma was used to derive the first conditional and unconditional non-Shannon-type inequalities. The second technique first appeared in Makarychev et al paper [6] and is based on a coding lemma from Ahlswede and Körner works. We first emphasize the importance of balanced inequalities and provide a simpler proof of a theorem of Chans for the case of Shannon-type inequalities. We compare the power of various proof systems based on a single technique.

On essentially conditional information inequalities

In 1997, Z. Zhang and R.W. Yeung found the first example of a conditional information inequality in four variables that is not “Shannon-type”. This linear inequality for entropies is called conditional (or constraint) since it holds only under condition that some linear equations are satisfied for the involved entropies. Later, the same authors and other researchers discovered several unconditional information inequalities that do not follow from Shannons inequalities for entropy. In this paper we show that some non Shannon-type conditional inequalities are “essentially” conditional, i.e., they cannot be extended to any unconditional inequality. We prove one new essentially conditional information inequality for Shannons entropy and discuss conditional information inequalities for Kolmogorov complexity.

Conditional Information Inequalities for Entropic and Almost Entropic Points

We study conditional linear information inequalities, i.e., linear inequalities for Shannon entropy that hold for distributions whose joint entropies meet some linear constraints. We prove that some conditional information inequalities cannot be extended to any unconditional linear inequalities. Some of these conditional inequalities hold for almost entropic points, while others do not. We also discuss some counterparts of conditional information inequalities for Kolmogorov complexity.

Improving the Linear Programming Technique in the Search for Lower Bounds in Secret Sharing.

We present a new improvement in the linear programming technique to derive lower bounds on the information ratio of secret sharing schemes. We obtain non-Shannon-type bounds without using information inequalities explicitly. Our new technique makes it possible to determine the optimal information ratio of linear secret sharing schemes for all access structures on 5 participants and all graph-based access structures on 6 participants. In addition, new lower bounds are presented also for some small matroid ports and, in particular, the optimal information ratios of the linear secret sharing schemes for the ports of the Vamos matroid are determined.

On the Information Ratio of Non-perfect Secret Sharing Schemes

A secret sharing scheme is non-perfect if some subsets of players that cannot recover the secret value have partial information about it. The information ratio of a secret sharing scheme is the ratio between the maximum length of the shares and the length of the secret. This work is dedicated to the search of bounds on the information ratio of non-perfect secret sharing schemes and the construction of efficient linear non-perfect secret sharing schemes. To this end, we extend the known connections between matroids, polymatroids and perfect secret sharing schemes to the non-perfect case. In order to study non-perfect secret sharing schemes in all generality, we describe their structure through their access function, a real function that measures the amount of information on the secret value that is obtained by each subset of players. We prove that there exists a secret sharing scheme for every access function. Uniform access functions, that is, access functions whose values depend only on the number of players, generalize the threshold access structures. The optimal information ratio of the uniform access functions with rational values has been determined by Yoshida, Fujiwara and Fossorier. By using the tools that are described in our work, we provide a much simpler proof of that result and we extend it to access functions with real values.