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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 20678

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Special Issue Editors

Dr. David Arroyo

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Guest Editor

Institute of Physical and Information Technologies (ITEFI), Spanish National Research Council (CSIC), 28006 Madrid, Spain
Interests: cryptography; privacy; smart cybersecurity; blockchain; IT governance; trust management and trustworthiness
Special Issues, Collections and Topics in MDPI journals

Dr. Jesus Diaz Vico

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Guest Editor

IBM Research Zurich, 8803 Rüschlikon, Switzerland
Interests: applied cryptography; privacy engineering; provable cryptography; distributed systems; theoretical computer science

Special Issue Information

Dear Colleagues,

The use of cryptography to protect our information is well known among scientists and technologists. However, knowledge of the limitations of the traditional approaches of encryption or obfuscation concerning the privacy level they ensure is not so widespread. In engineering, it is even common to come across claims of purportedly anonymized data sets that have been only subject to removal of their main identifiers. This becomes even more dangerous given the vast and rich amounts of data that large organizations and governments control. Here, a tradeoff is typically inherent: If robust desensitization techniques are applied, the data set loses utility; but if we are lenient in the process of removing sensitive data in order to ensure utility, privacy is threatened—especially if we take into account the constraints that can be set by the use case, in terms of who controls what information, or the required throughput of the process. Somehow orthogonally, but also affecting the right to privacy and boosted by the easy access to vast amounts of data, there is a growing need to evaluate the veracity of information, as it becomes easier to artificially generate media that appear legitimate to human perception. In any of these situations, objective evaluations of either privacy or data quality, derived from information-theoretic techniques, is highly desirable.

All these challenges call for an interdisciplinary approach where cryptographers, privacy engineers, and data scientists need to collaborate in order to address the theoretical, engineering, and business needs, where robust data curation mechanisms, encompassing techniques such as user-centric security ceremonies, privacy-preserving, and cryptographic techniques, have to come together to provide a multilayered approach.

Dr. David Arroyo
Dr. Jesus Diaz Vico
Guest Editors

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Keywords

Theoretic data privacy
Cryptographic engineering
Privacy engineering
Differential privacy
Utility–privacy tradeoff
Big data
User-centric security

Published Papers (7 papers)

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Research

14 pages, 420 KiB

Open AccessArticle

A Compression-Based Method for Detecting Anomalies in Textual Data

by Gonzalo de la Torre-Abaitua, Luis Fernando Lago-Fernández and David Arroyo

Entropy 2021, 23(5), 618; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050618 - 16 May 2021

Cited by 8 | Viewed by 2546

Abstract

Nowadays, information and communications technology systems are fundamental assets of our social and economical model, and thus they should be properly protected against the malicious activity of cybercriminals. Defence mechanisms are generally articulated around tools that trace and store information in several ways, the simplest one being the generation of plain text files coined as security logs. Such log files are usually inspected, in a semi-automatic way, by security analysts to detect events that may affect system integrity, confidentiality and availability. On this basis, we propose a parameter-free method to detect security incidents from structured text regardless its nature. We use the Normalized Compression Distance to obtain a set of features that can be used by a Support Vector Machine to classify events from a heterogeneous cybersecurity environment. In particular, we explore and validate the application of our method in four different cybersecurity domains: HTTP anomaly identification, spam detection, Domain Generation Algorithms tracking and sentiment analysis. The results obtained show the validity and flexibility of our approach in different security scenarios with a low configuration burden. Full article

(This article belongs to the Special Issue Information Theoretic Security and Privacy of Information Systems)

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15 pages, 350 KiB

Open AccessArticle

Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission

by Bin Jiang, Linbo Qu, Yufei Huang, Yifei Zheng, Li You and Wenjin Wang

Entropy 2020, 22(10), 1145; https://0-doi-org.brum.beds.ac.uk/10.3390/e22101145 - 12 Oct 2020

Cited by 2 | Viewed by 1827

Abstract

Herein, we focus on energy efficiency optimization for massive multiple-input multiple-output (MIMO) downlink secure multicast transmission exploiting statistical channel state information (CSI). Privacy engineering in the field of communication is a hot issue under study. The common signal transmitted by the base station is multicast transmitted to multiple legitimate user terminals in our system, but an eavesdropper might eavesdrop this signal. To achieve the energy efficiency utility–privacy trade-off of multicast transmission, we set up the problem of maximizing the energy efficiency which is defined as the ratio of the secure transmit rate to the power consumption. To simplify the formulated nonconvex problem, we use a lower bound of the secure multicast rate as the molecule of the design objective. We then obtain the eigenvector of the optimal transmit covariance matrix into a closed-form, simplifying the matrix-valued multicast transmission strategy problem into a power allocation problem in the beam domain. By utilizing the Minorize-Maximize method, an iterative algorithm is proposed to decompose the secure energy efficiency optimization problem into a sequence of iterative fractional programming subproblems. By using Dinkelbach’s transform, each subproblem becomes an iterative problem with the concave objective function, and it can be solved by classical convex optimization. We guarantee the convergence of the two-level iterative algorithm that we propose. Besides, we reduce the computational complexity of the algorithm by substituting the design objective with its deterministic equivalent. The numerical results show that the approach we propose performs well compared with the conventional methods. Full article

(This article belongs to the Special Issue Information Theoretic Security and Privacy of Information Systems)

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13 pages, 798 KiB

Open AccessArticle

High Efficiency Continuous-Variable Quantum Key Distribution Based on ATSC 3.0 LDPC Codes

by Kun Zhang, Xue-Qin Jiang, Yan Feng, Runhe Qiu and Enjian Bai

Entropy 2020, 22(10), 1087; https://0-doi-org.brum.beds.ac.uk/10.3390/e22101087 - 27 Sep 2020

Cited by 8 | Viewed by 2412

Abstract

Due to the rapid development of quantum computing technology, encryption systems based on computational complexity are facing serious threats. Based on the fundamental theorem of quantum mechanics, continuous-variable quantum key distribution (CVQKD) has the property of physical absolute security and can effectively overcome the dependence of the current encryption system on the computational complexity. In this paper, we construct the spatially coupled (SC)-low-density parity-check (LDPC) codes and quasi-cyclic (QC)-LDPC codes by adopting the parity-check matrices of LDPC codes in the Advanced Television Systems Committee (ATSC) 3.0 standard as base matrices and introduce these codes for information reconciliation in the CVQKD system in order to improve the performance of reconciliation efficiency, and then make further improvements to final secret key rate and transmission distance. Simulation results show that the proposed LDPC codes can achieve reconciliation efficiency of higher than 0.96. Moreover, we can obtain a high final secret key rate and a long transmission distance through using our proposed LDPC codes for information reconciliation. Full article

(This article belongs to the Special Issue Information Theoretic Security and Privacy of Information Systems)

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34 pages, 1581 KiB

Open AccessArticle

Designing Two Secure Keyed Hash Functions Based on Sponge Construction and the Chaotic Neural Network

by Nabil Abdoun, Safwan El Assad, Thang Manh Hoang, Olivier Deforges, Rima Assaf and Mohamad Khalil

Entropy 2020, 22(9), 1012; https://0-doi-org.brum.beds.ac.uk/10.3390/e22091012 - 10 Sep 2020

Cited by 17 | Viewed by 4222

Abstract

In this paper, we propose, implement, and analyze the structures of two keyed hash functions using the Chaotic Neural Network (CNN). These structures are based on Sponge construction, and they produce two variants of hash value lengths, i.e., 256 and 512 bits. The first structure is composed of two-layered CNN, while the second one is formed by one-layered CNN and a combination of nonlinear functions. Indeed, the proposed structures employ two strong nonlinear systems, precisely a chaotic system and a neural network system. In addition, the proposed study is a new methodology of combining chaotic neural networks and Sponge construction that is proved secure against known attacks. The performance of the two proposed structures is analyzed in terms of security and speed. For the security measures, the number of hits of the two proposed structures doesn’t exceed 2 for 256-bit hash values and does not exceed 3 for 512-bit hash values. In terms of speed, the average number of cycles to hash one data byte (NCpB) is equal to 50.30 for Structure 1, and 21.21 and 24.56 for Structure 2 with 8 and 24 rounds, respectively. In addition, the performance of the two proposed structures is compared with that of the standard hash functions SHA-3, SHA-2, and with other classical chaos-based hash functions in the literature. The results of cryptanalytic analysis and the statistical tests highlight the robustness of the proposed keyed hash functions. It also shows the suitability of the proposed hash functions for the application such as Message Authentication, Data Integrity, Digital Signature, and Authenticated Encryption with Associated Data. Full article

(This article belongs to the Special Issue Information Theoretic Security and Privacy of Information Systems)

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21 pages, 1425 KiB

Open AccessArticle

A Novel Image-Encryption Scheme Based on a Non-Linear Cross-Coupled Hyperchaotic System with the Dynamic Correlation of Plaintext Pixels

by Wenjin Hou, Shouliang Li, Jiapeng He and Yide Ma

Entropy 2020, 22(7), 779; https://0-doi-org.brum.beds.ac.uk/10.3390/e22070779 - 17 Jul 2020

Cited by 8 | Viewed by 2466

Abstract

Based on a logistic map and Feigenbaum map, we proposed a logistic Feigenbaum non-linear cross-coupled hyperchaotic map (LF-NCHM) model. Experimental verification showed that the system is a hyperchaotic system. Compared with the existing cross-coupled mapping, LF-NCHM demonstrated a wider hyperchaotic range, better ergodicity and richer dynamic behavior. A hyperchaotic sequence with the same number of image pixels was generated by LF-NCHM, and a novel image-encryption algorithm with permutation that is dynamically related to plaintext pixels was proposed. In the scrambling stage, the position of the first scrambled pixel was related to the sum of the plaintext pixel values, and the positions of the remaining scrambled pixels were related to the pixel values after the previous scrambling. The scrambling operation also had a certain diffusion effect. In the diffusion phase, using the same chaotic sequence as in the scrambling stage increased the usage rate of the hyperchaotic sequence and improved the calculation efficiency of the algorithm. A large number of experimental simulations and cryptanalyses were performed, and the results proved that the algorithm had outstanding security and extremely high encryption efficiency. In addition, LF-NCHM could effectively resist statistical analysis attacks, differential attacks and chosen-plaintext attacks. Full article

(This article belongs to the Special Issue Information Theoretic Security and Privacy of Information Systems)

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28 pages, 3637 KiB

Open AccessArticle

Anomaly Detection for Individual Sequences with Applications in Identifying Malicious Tools

by Shachar Siboni and Asaf Cohen

Entropy 2020, 22(6), 649; https://0-doi-org.brum.beds.ac.uk/10.3390/e22060649 - 12 Jun 2020

Cited by 4 | Viewed by 3405

Abstract

Anomaly detection refers to the problem of identifying abnormal behaviour within a set of measurements. In many cases, one has some statistical model for normal data, and wishes to identify whether new data fit the model or not. However, in others, while there are normal data to learn from, there is no statistical model for this data, and there is no structured parameter set to estimate. Thus, one is forced to assume an individual sequences setup, where there is no given model or any guarantee that such a model exists. In this work, we propose a universal anomaly detection algorithm for one-dimensional time series that is able to learn the normal behaviour of systems and alert for abnormalities, without assuming anything on the normal data, or anything on the anomalies. The suggested method utilizes new information measures that were derived from the Lempel–Ziv (LZ) compression algorithm in order to optimally and efficiently learn the normal behaviour (during learning), and then estimate the likelihood of new data (during operation) and classify it accordingly. We apply the algorithm to key problems in computer security, as well as a benchmark anomaly detection data set, all using simple, single-feature time-indexed data. The first is detecting Botnets Command and Control (C&C) channels without deep inspection. We then apply it to the problems of malicious tools detection via system calls monitoring and data leakage identification.We conclude with the New York City (NYC) taxi data. Finally, while using information theoretic tools, we show that an attacker’s attempt to maliciously fool the detection system by trying to generate normal data is bound to fail, either due to a high probability of error or because of the need for huge amounts of resources. Full article

(This article belongs to the Special Issue Information Theoretic Security and Privacy of Information Systems)

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53 pages, 13998 KiB

Open AccessArticle

Novel Models of Image Permutation and Diffusion Based on Perturbed Digital Chaos

by Thang Manh Hoang and Safwan El Assad

Entropy 2020, 22(5), 548; https://0-doi-org.brum.beds.ac.uk/10.3390/e22050548 - 13 May 2020

Cited by 10 | Viewed by 3017

Abstract

Most of chaos-based cryptosystems utilize stationary dynamics of chaos for the permutation and diffusion, and many of those are successfully attacked. In this paper, novel models of the image permutation and diffusion are proposed, in which chaotic map is perturbed at bit level on state variables, on control parameters or on both. Amounts of perturbation are initially the coordinate of pixels in the permutation, the value of ciphered word in the diffusion, and then a value extracted from state variables in every iteration. Under the persistent perturbation, dynamics of chaotic map is nonstationary and dependent on the image content. The simulation results and analyses demonstrate the effectiveness of the proposed models by means of the good statistical properties of transformed image obtained after just only a single round. Full article

(This article belongs to the Special Issue Information Theoretic Security and Privacy of Information Systems)

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