Submit to Entropy Review for Entropy Propose a Special Issue

Journal Menu

Journal Browser

Information-Theoretic Security and Privacy

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 (20 February 2026) | Viewed by 10063

Share This Special Issue

Special Issue Editors

Dr. Mohamed Seif

E-Mail Website
Guest Editor

Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
Interests: differential privacy; information theoretic privacy and secrecy; information networks; federated learning; statistical inference

Dr. Hua Sun

E-Mail Website
Guest Editor

Department Electrical Engineering, University of North Texas, Denton, TX 76203, USA
Interests: information theory; security and privacy; coding theory; distributed storage and computation; wireless communications

Special Issue Information

Dear Colleagues,

Our current digital landscape has made information security and privacy crucial. In a world where data breaches and cyber threats are growing more sophisticated, safeguarding sensitive information has become paramount for organizations and individuals. This Special Issue delves into the multifaceted challenges and innovative information security and privacy solutions for various information systems, including wireless networks, machine learning, smart grids, and social graphs.

Ensuring that information systems are secure, reliable, and private is essential. Information-theoretic measures offer a powerful framework to address these challenges, providing insights that quantify these essential qualities and rigorously evaluate and guarantee their integrity. This approach illuminates the path to building more trustworthy and resilient information systems across various domains.

We invite previously unpublished contributions at the intersection of information theory, networks, wireless communications, and data privacy, including (but not limited to) the following topics:

Theoretical foundations of information-theoretic privacy;
Privacy-preserving distributed/federated learning
The design of privacy mechanisms for big data analytics (including healthcare and social networks);
Performance evaluations of privacy-preserving mechanisms;
Privacy-preserving data publishing;
Energy-efficient physical-layer security;
The integration of differential privacy and physical-layer security;
Cryptographic protocols for differential privacy;
Private information retrieval;
Privacy-preserving distributed computing.

Dr. Mohamed Seif
Dr. Hua Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

differential privacy
machine learning
federated learning
secure aggregation
age of information
gossip over networks
statistical inference over networks
information-theoretic privacy and security
wireless network
social networks and healthcare

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Editorial

Jump to: Research

2 pages, 115 KB

Open AccessEditorial

Information-Theoretic Security and Privacy in Modern Data-Driven Systems

by Mohamed Seif and Hua Sun

Entropy 2026, 28(5), 483; https://doi.org/10.3390/e28050483 - 22 Apr 2026

Viewed by 322

Abstract

This Special Issue titled “Information-Theoretic Security and Privacy” brings together nine peer-reviewed contributions that span both fundamental theory and practical system design [...] Full article

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

Research

Jump to: Editorial

20 pages, 893 KB

Open AccessArticle

Step-Wise Dual Dynamic DPSGD: Enhancing Performance on Imbalanced Medical Datasets with Differential Privacy

by Xiaobo Huang and Fang Xie

Entropy 2026, 28(4), 409; https://doi.org/10.3390/e28040409 - 4 Apr 2026

Viewed by 369

Abstract

The application of differential privacy in deep learning often leads to significant performance degradation on class-imbalanced medical datasets. Methods such as adding noise to gradients for differential privacy are effective on large datasets, like MNIST and CIFAR-100, but perform poorly on small, imbalanced medical datasets, like HAM10000 and ISIC2019. This is because the imbalanced distribution causes the gradients from the few-shot classes to be clipped, resulting in the loss of crucial information, while the majority classes dominate the learning process. This leads the model to fall into suboptimal solutions early. To address this issue, we propose SDD-DPSGD, which uses a step-wise dynamic exponential scheduling mechanism for noise and clipping thresholds to preserve gradient information. By allocating more privacy budget and employing higher clipping thresholds during the initial training phases, the model can avoid suboptimal solutions and improve its performance. Experiments show that SDD-DPSGD outperforms comparable algorithms on the HAM10000 dataset, and the ISIC2019 dataset. Full article

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

► Show Figures

Figure 1

21 pages, 1058 KB

Open AccessArticle

Sequential Change Detection with Local Differential Privacy

by Lixing Zhang, Xuran Liu, Ruizhi Zhang and Liyan Xie

Entropy 2026, 28(4), 402; https://doi.org/10.3390/e28040402 - 2 Apr 2026

Viewed by 458

Abstract

Sequential change detection is a fundamental problem in statistics and signal processing, with the CUSUM procedure widely used to achieve minimax detection delay under a prescribed false alarm rate when pre- and post-change distributions are fully known. However, in many practical settings, raw observations cannot be shared with a trusted central curator, and privacy must be enforced at the data source, which prevents the computation of exact CUSUM statistics. We therefore introduce a local differentially private (DP) variant called LDP-CUSUM, which first applies a local DP mechanism to transform the raw data into privatized observations and then applies a CUSUM procedure to detect the change. We derive closed-form bounds on the average run length to false alarm and on the worst-case average detection delay, explicitly characterizing the tradeoff among privacy level, false alarm rate, and detection efficiency. Numerical simulations and a real-data case study were conducted to demonstrate the detection efficiency of our proposed LDP-CUSUM across various scenarios. Full article

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

► Show Figures

Figure 1

21 pages, 1036 KB

Open AccessArticle

Spec-LAMP: Robust Spectre Attack Detection Under Web-Based LLM Workload via L1D Miss Pending Event

by Jiajia Jiao, Quan Zhou and Yulian Li

Entropy 2026, 28(3), 254; https://doi.org/10.3390/e28030254 - 26 Feb 2026

Viewed by 518

Abstract

As Large Language Models (LLMs) become increasingly integrated into web environments, they introduce complex microarchitectural noise that challenges existing hardware security mechanisms. This paper investigates the impact of concurrent web-based LLM workloads on the detection accuracy of Spectre attacks. Firstly, we constructed a representative dataset by executing multiple web-accessible LLMs (e.g., DeepSeek, Kimi, Doubao and Qwen) alongside Spectre attacks, capturing the specific interference patterns introduced by these AI workloads. Experimental analysis reveals that traditional Hardware Performance Counter (HPC)-based detectors, relying primarily on branch prediction and Last-Level Cache (LLC) events, suffer significant accuracy degradation due to the masking effects of LLM-induced noise. To address this limitation, we then propose a novel Spectre attack detector Spec-LAMP via augmenting conventional HPC feature sets with the L1D Miss Pending event. This new metric specifically captures unresolved speculative memory dependencies, a distinctive characteristic of Spectre attacks that remains discernible even under web-accessible LLM interference. Comparative statistical analysis demonstrates that incorporating this event significantly enhances the separability between malicious and benign executions. Finally, experimental results show that our proposed feature augmentation effectively restores detection performance, increasing average accuracy from 85.15% to 98.43% and demonstrating superior robustness compared to traditional approaches in realistic web-based LLM scenarios. Full article

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

► Show Figures

Figure 1

21 pages, 2513 KB

Open AccessArticle

Towards Information-Theoretic Security and Privacy in IoT: A Three-Factor AKA Protocol Supporting Forgotten Password Reset

by Yicheng Yu, Kai Wei, Hongtu Li and Kai Zhang

Entropy 2026, 28(2), 205; https://doi.org/10.3390/e28020205 - 11 Feb 2026

Viewed by 392

Abstract

The growth of the Internet of Things (IoT) has created many problems. A wise example is presented by the design of secure, efficient authentication and key agreement (AKA) protocols. A novel three-factor AKA protocol for the IoT is presented in this paper. The scheme integrates password, biometric, and device-based factors that achieved strong security, which gives anonymity to the user, achieves forward secrecy, and makes the scheme resilient to various attacks like replay, impersonation, and de-synchronization. It also adds a safe lost-password-reset functionality, which makes the protocol more usable. Security analysis proves its strength against the typical adversary, while performance evaluation shows that the solution is better than existing solutions in terms of computational and communication efficiency. The work proposes a practical and scalable security solution for IoT systems, which satisfies the high security standard but within the constraints of an IoT system. Full article

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

► Show Figures

Figure 1

20 pages, 378 KB

Open AccessArticle

On the Storage–Communication Trade-Off in Graph-Based X-Secure T-Private Linear Computation

by Yueyang Liu, Haobo Jia and Zhuqing Jia

Entropy 2025, 27(9), 975; https://doi.org/10.3390/e27090975 - 18 Sep 2025

Cited by 1 | Viewed by 773

Abstract

The problem of graph-based X-secure T-private linear computation (GXSTPLC) is to allow a user to retrieve a linear combination of K messages from a set of N distributed servers that store the messages in a graph-based fashion, i.e., each message is restricted to be distributed among a subset of servers. T-privacy requires that the coefficients of the linear combination are not revealed to any group of up to T colluding servers, and X-security guarantees that any set of up to X colluding servers learns nothing about the messages. In this paper, we propose an achievability scheme for GXSTPLC that enables a storage–communication trade-off by exploiting non-replicated storage codes. Novel aspects of our achievability scheme include the usage of the idea of cross-subspace alignment null shaper that addresses various challenges posed by the graph-based storage structure. In addition, unlike previous works, our scheme allows a direct transformation into a quantum one to achieve a superdense coding gain by leveraging the idea of N-Sum Box abstraction of quantum “over-the-air” computing. Full article

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

► Show Figures

Figure 1

28 pages, 12461 KB

Open AccessArticle

HCSS-GB and IBESS: Secret Image Sharing Schemes with Enhanced Shadow Management and Visual-Gradient Access Control

by Huanrong Pan, Wei Yan, Rui Wang and Yongqiang Yu

Entropy 2025, 27(9), 893; https://doi.org/10.3390/e27090893 - 23 Aug 2025

Viewed by 1113

Abstract

Image protection in privacy-sensitive domains, such as healthcare and military, exposes critical limitations in existing secret image sharing (SIS) schemes, including cumbersome shadow management, coarse-grained access control, and an inefficient storage-speed trade-off, which limits SIS in practical scenarios. Thus, this paper proposes two SIS schemes to address the above issues: the hierarchical control sharing scheme with Gaussian blur (HCSS-GB) and the image bit expansion-based sharing scheme (IBESS). For scenarios with limited storage space, HCSS-GB employs Gaussian blur to generate gradient-blurred cover images and integrates a controllable sharing model to produce meaningful shadow images without pixel expansion based on Shamir’s secret sharing. Furthermore, to accommodate real-time application scenarios, IBESS employs bit expansion to combine the high bits of generated shadow images with those of blurred carrier images, enhancing operational efficiency at the cost of increased storage overhead. Experimental results demonstrate that both schemes achieve lossless recovery (with PSNR of ∞, MSE of 0, and SSIM of 1), validating their reliability. Specifically, HCSS-GB maintains a 1:1 storage ratio with the original image, making it highly suitable for storage-constrained environments; IBESS exhibits exceptional efficiency, with sharing time as low as 2.1 s under the

(7, 8)

threshold, ideal for real-time tasks. Comparative analyses further show that using carrier images with high standard deviation contrast (

C_{σ}

) and Laplacian-based sharpness (

S_{L}

) significantly enhances shadow distinguishability, strengthening the effectiveness of hierarchical access control. Both schemes provide valuable solutions for secure image sharing and efficient shadow management, with their validity and practicality confirmed by experimental data. Full article

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

► Show Figures

Figure 1

26 pages, 486 KB

Open AccessArticle

Towards Characterizing the Download Cost of Cache-Aided Private Updating

by Bryttany Stark, Ahmed Arafa and Karim Banawan

Entropy 2025, 27(8), 828; https://doi.org/10.3390/e27080828 - 4 Aug 2025

Viewed by 823

Abstract

We consider the problem of privately updating a message out of K messages from N replicated and non-colluding databases where a user has an outdated version of the message

{\hat{W}}_{θ}

of length L bits that differ from the current version [...] Read more.

We consider the problem of privately updating a message out of K messages from N replicated and non-colluding databases where a user has an outdated version of the message

{\hat{W}}_{θ}

of length L bits that differ from the current version

W_{θ}

in at most f bits. The user also has a cache containing coded combinations of the K messages (with a pre-specified structure), which are unknown to the N databases (unknown prefetching). The cache Z contains ℓ linear combinations from all K messages in the databases with

r = \frac{l}{L}

being the caching ratio. The user needs to retrieve

W_{θ}

correctly using a private information retrieval (PIR) scheme without leaking information about the message index

θ

to any individual database. Our objective is to jointly design the prefetching (i.e., the structure of said linear combinations) and the PIR strategies to achieve the least download cost. We propose a novel achievable scheme based on syndrome decoding where the cached linear combinations in Z are designed to be bits pertaining to the syndrome of

W_{θ}

according to a specific linear block code. We derive a general lower bound on the optimal download cost for

0 \leq r \leq 1

, in addition to achievable upper bounds. The upper and lower bounds match for the cases when r is exceptionally low or high, or when

K = 3

messages for arbitrary r. Such bounds are derived by developing novel cache-aided arbitrary message length PIR schemes. Our results show a significant reduction in the download cost if

f < \frac{L}{2}

when compared with downloading

W_{θ}

directly using typical cached-aided PIR approaches. Full article

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

► Show Figures

Figure 1

37 pages, 979 KB

Open AccessFeature PaperArticle

Variable-Length Coding with Zero and Non-Zero Privacy Leakage

by Amirreza Zamani and Mikael Skoglund

Entropy 2025, 27(2), 124; https://doi.org/10.3390/e27020124 - 24 Jan 2025

Cited by 4 | Viewed by 1954

Abstract

A private compression design problem is studied, where an encoder observes useful data Y, wishes to compress them using variable-length code, and communicates them through an unsecured channel. Since Y are correlated with the private attribute X, the encoder uses a [...] Read more.

C

and sends it over the channel. An adversary is assumed to have access to the output of the encoder, i.e.,

C

, and tries to estimate X. Furthermore, it is assumed that both encoder and decoder have access to a shared secret key W. In this work, the design goal is to encode message

C

with the minimum possible average length that satisfies certain privacy constraints. We consider two scenarios: 1. zero privacy leakage, i.e., perfect privacy (secrecy); 2. non-zero privacy leakage, i.e., non-perfect privacy constraint. Considering the perfect privacy scenario, we first study two different privacy mechanism design problems and find upper bounds on the entropy of the optimizers by solving a linear program. We use the obtained optimizers to design

C

. In the two cases, we strengthen the existing bounds: 1.

| X | \geq | Y |

; 2. The realization of

(X, Y)

follows a specific joint distribution. In particular, considering the second case, we use two-part construction coding to achieve the upper bounds. Furthermore, in a numerical example, we study the obtained bounds and show that they can improve existing results. Finally, we strengthen the obtained bounds using the minimum entropy coupling concept and a greedy entropy-based algorithm. Considering the non-perfect privacy scenario, we find upper and lower bounds on the average length of the encoded message using different privacy metrics and study them in special cases. For achievability, we use two-part construction coding and extended versions of the functional representation lemma. Lastly, in an example, we show that the bounds can be asymptotically tight. Full article

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

► Show Figures

Figure 1

22 pages, 2513 KB

Open AccessArticle

CURATE: Scaling-Up Differentially Private Causal Graph Discovery

by Payel Bhattacharjee and Ravi Tandon

Entropy 2024, 26(11), 946; https://doi.org/10.3390/e26110946 - 5 Nov 2024

Viewed by 1367

Abstract

Causal graph discovery (CGD) is the process of estimating the underlying probabilistic graphical model that represents the joint distribution of features of a dataset. CGD algorithms are broadly classified into two categories: (i) constraint-based algorithms, where the outcome depends on conditional independence (CI) tests, and (ii) score-based algorithms, where the outcome depends on optimized score function. Because sensitive features of observational data are prone to privacy leakage, differential privacy (DP) has been adopted to ensure user privacy in CGD. Adding the same amount of noise in this sequential-type estimation process affects the predictive performance of algorithms. Initial CI tests in constraint-based algorithms and later iterations of the optimization process of score-based algorithms are crucial; thus, they need to be more accurate and less noisy. Based on this key observation, we present CURATE (CaUsal gRaph AdapTivE privacy), a DP-CGD framework with adaptive privacy budgeting. In contrast to existing DP-CGD algorithms with uniform privacy budgeting across all iterations, CURATE allows for adaptive privacy budgeting by minimizing error probability (constraint-based), maximizing iterations of the optimization problem (score-based) while keeping the cumulative leakage bounded. To validate our framework, we present a comprehensive set of experiments on several datasets and show that CURATE achieves higher utility compared to existing DP-CGD algorithms with less privacy leakage. Full article

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

► Show Figures

Journal Menu

Journal Browser

Information-Theoretic Security and Privacy

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Editorial

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI