This paper generalizes the definition of privacy in graph-replicated Private Information Retrieval (PIR) by allowing each server to have an arbitrary, specific set of message indices it must keep priv…

The paper introduces SPIDER, a novel single-server Private Information Retrieval (PIR) scheme that achieves state-of-the-art communication complexity without requiring specialized server cooperation o…

GPIR is a GPU-accelerated Private Information Retrieval (PIR) system that significantly boosts throughput by introducing a stage-aware hybrid execution model and optimizing data layouts for modern GPU…

The paper proposes a novel ring-based information-theoretic Private Information Retrieval (itED-PIR) scheme that overcomes the key size and communication overhead limitations of existing field-based A…

The paper proposes a novel, unconditionally secure information-theoretic Authenticated Private Information Retrieval (itAPIR) scheme that upgrades existing, less secure itPIR-RV schemes without overhe…

This paper presents a cryptanalytic attack demonstrating that a specific code-based Private Information Retrieval (PIR) scheme can be broken, allowing the server to efficiently determine the requested…

This paper provides a comprehensive, practitioner-oriented framework and survey to guide the selection and evaluation of differentially private methods for releasing sensitive graph data.

The paper presents the first general mechanism for counting arbitrary acyclic graph patterns under Local Differential Privacy (LDP), addressing challenges in pattern construction and node duplication.

PRAG is an end-to-end privacy-preserving Retrieval-Augmented Generation (RAG) system that maintains high retrieval accuracy and scalability in cloud environments by encrypting both documents and queri…

The paper introduces the PML envelope, a novel definition that provides a robust and operationally meaningful measure of information leakage about a secret, satisfying both post-processing robustness…

The paper proposes EPDQ, a tensor-based scheme that efficiently and privately computes exact shortest distance queries on large-scale encrypted graphs by combining specialized indexing and tensor repr…

Pepper is a novel, high-bandwidth anonymous broadcast protocol that achieves cryptographic sender anonymity and significantly improves messaging throughput compared to existing state-of-the-art system…

The paper proposes a Quantitative Information Flow (QIF) framework to systematically and rigorously compare Local Differential Privacy (LDP) frequency estimation protocols, moving beyond simple $\vare…

The paper introduces PolyVeil, a protocol for private Boolean summation that uses permutation matrices in the Birkhoff polytope, achieving strong security guarantees while highlighting a fundamental t…

TAPAS introduces an efficient, asymmetric two-server private aggregation scheme that significantly reduces computational and communication costs for large-scale federated learning compared to existing…

The paper addresses secure distributed hypothesis testing, proving impossibility in the standard setting and achieving secure testing for simple and general classes by incorporating a shared secret ke…

The paper introduces Bayesian Membership Privacy (BMP), a sampling-aware framework that accurately quantifies node-level membership privacy in Graph Neural Networks by treating graph sampling probabil…

The paper introduces PAS, a structured privacy mechanism that encodes user location using relative anchors, enabling location privacy in spatial RAG systems while maintaining high retrieval performanc…

The paper proposes PPRoute, a privacy-preserving LLM routing framework that significantly speeds up secure model selection while maintaining high performance comparable to non-private methods.

The paper proposes a novel, perfectly secure Information-Theoretic Distributed Point Function (ITDPF) that converts point functions into shares using asymptotically shorter secret keys compared to exi…