This paper proposes two approaches for applying two-server Private Information Retrieval (PIR) protocols to synthetic DNA-based data storage, addressing privacy, efficiency, and feasibility challenges…

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…

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 proposes a Graph-based Weak Private Information Retrieval (G-WPIR) scheme for distributed storage systems with graph-based replication, identifying trade-offs between rate and privacy under…

The paper introduces local private information retrieval (local PIR), redefining user privacy in graph-replicated systems to focus on hiding the message index from servers, and demonstrates that local…

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…

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…

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…

This paper proposes PA-HDP, a framework for privacy-preserving retrieval-augmented generation using prompt-aware dynamic hierarchical differential privacy.

This case study systematically measures how placing anonymization at different points (dataset vs. generated answer) within the RAG pipeline affects the privacy-utility trade-off, demonstrating that p…

The paper introduces a secure Federated RAG system that enables confidential retrieval and LLM inference across distributed, private data silos.

This paper introduces private and efficient algorithms for exact community detection in the stochastic block model under privacy constraints, achieving near-linear time and space complexity.

The paper introduces PostRI, a novel method that allows for computing a Randomization Interval (RI) for differentially private median queries after the median has already been estimated, significantly…

The paper proposes TRIP-RAG, a dynamic anonymization framework that selectively anonymizes sensitive entities in knowledge bases used for RAG, significantly improving utility while maintaining strong…

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 quantifies the cost of privacy in language identification and generation using differentially private (DP) methods, finding that the cost is surprisingly mild, particularly absent under appr…

The paper proposes RPSG, a method that uses private seeds and differential privacy to generate highly realistic and strongly privacy-preserving synthetic data replicas of private text for LLMs.