Information Theory and Differential Privacy
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 25
Special Issue Editors
Interests: information theory; privacy and security
Interests: privacy and security; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Information theory, a discipline born from the pioneering work of Claude Shannon in the 1940s, bridges the realms of engineering and mathematics. Its primary goal lies in quantifying, storing, and transmitting information effectively. Shannon’s revolutionary insights formed the foundation for modern information theory, offering a rigorous mathematical framework to understand the capacities of communication systems and the fundamental limits of data compression and error correction. Consequently, this field has found far-reaching applications, from telecommunications and data compression to privacy-preserving algorithms and machine learning, shaping the future of digital communication and information security.
The rapid growth of data from networked sensors monitoring environments, human activity, robotics, and software systems raises significant privacy concerns, as raw data can be exploited through advanced inference attacks. Information-theoretic privacy frameworks provide powerful tools to mitigate such risks, guiding the design of mechanisms that balance utility with provable protection.
Viewed through the lens of information theory, privacy mechanism design naturally connects to the measurement and control of information leakage. Differential privacy has emerged as a widely adopted framework in this context, offering a mathematically rigorous means to bound the probability of revealing sensitive attributes when querying databases. More recently, researchers have explored its synergy with information-theoretic measures such as mutual information, maximal leakage, and rate-distortion theory to yield tighter, application-specific guarantees. This unified perspective opens pathways toward privacy mechanisms that are not only theoretically optimal but also computationally practical, scalable across heterogeneous data sources, and resilient against adaptive adversaries.
This Special Issue invites original research articles that study the integration of differential privacy, including its variants and generalizations within the framework of information theory. We particularly welcome contributions that present novel theoretical insights, rigorous privacy–utility analyses, and innovative applications that bridge the gap between privacy guarantees and information-theoretic principles.
Dr. Amirreza Zamani
Dr. Mina Sheikhalishahi
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 100 words) can be sent to the Editorial Office for announcement on this website.
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
- information-theoretic differential privacy
- differentially private distributed computing
- differential privacy in fairness
- differential privacy in network coding
- differentially private learning algorithms
- differential privacy in Lagrange coded computing
- differential privacy in network information theory
- differential privacy in reinforcement learning and federated learning
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