Private Information Retrieval and Its Applications
A Special Issue of Entropy (ISSN 1099-4300) belonging to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 961
Editors
Interests: information theory; coding theory; differential privacy; trustworthy AI
Interests: capacity of wireless networks; private/secure/coded/distributed storage/retrieval/computation; network coding; network information theory; quantum information theory
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Special Issue Information
Dear Colleagues,
In many domains that involve sensitive information—such as medicine, finance, and defense—users often need to access information without revealing their requirements or interests. Private Information Retrieval (PIR) provides a rigorous framework for this setting: it allows a user to download one item of interest from a collection of items that are replicated in a set of non-colluding databases, while ensuring that the identity of the requested item remains hidden. Classic examples include an investor retrieving specific stock records without signaling possible investment moves, or an inventor querying patent databases without disclosing the direction of their own work prior to publication. By protecting the user’s intent, PIR serves as a cornerstone primitive for privacy-preserving data access.
PIR has been extensively studied from an information-theoretic perspective, with a central focus on characterizing its capacity and developing capacity-achieving schemes. These results play a role analogous to Shannon’s channel capacity in communication theory: they establish the fundamental performance limits of PIR protocols. The classical PIR model has been extended in multiple directions: (i) stronger adversary settings, including colluding, adversarial, or eavesdropping servers; (ii) alternative storage models such as coded PIR and single-database PIR; (iii) variants with additional privacy or communication requirements, including symmetric PIR and PIR with side information; and (iv) quantum PIR, which explores new trade-offs with quantum communication. More recently, PIR concepts have also been applied in machine learning, for tasks like private federated learning, private nearest-neighbor search, and privacy-preserving inference.
Building on these advances, this Special Issue aims to showcase the latest developments in PIR. We invite contributions that explore novel constructions under diverse threat and storage models, new capacity results, and emerging applications that connect PIR to modern data-driven systems. The goal is to provide a platform for exchanging ideas that will shape the future directions of PIR research and its role in enabling privacy-preserving technologies.
Dr. Sajani Vithana
Prof. Dr. Syed A. Jafar
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-anonymized 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
- private information retrieval
- capacity-achieving schemes
- threat models
- quantum pir
- privacy-preserving machine learning
- private distributed computations
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