Opportunity: Privacy-Preserving AI for Discovery and Analysis of Publicly Available Information (PAI)

This research topic seeks novel methodologies for conducting comprehensive and secure analysis across the vast landscape of Publicly Available Information (PAI). PAI is inherently multilingual and takes diverse forms, including online publications, news, social media, and AI-generated content. The central challenge is twofold: first, to enable analysts to perform PAI retrieval while rigorously protecting their identity and intent from being tracked or linked back to them. Second, as analysts increasingly rely on advanced AI models (e.g., Large Language Models) for analysis, a new challenge emerges: protecting the query content and analytical intent from the AI service providers themselves. This requires a new generation of privacy-preserving technologies that can operate on untrusted platforms.
We are particularly interested in novel technologies and approaches that address:
•Privacy-Preserving ML for PAI Analysis: Advanced cryptographic techniques for secure ML training and inference on PAI. This includes, but is not limited to, Homomorphic Encryption (HE) for computation on encrypted data, Secure Multi-Party Computation (MPC), and hybrid cryptographic protocols that combine the strengths of both.
•Privacy-Preserving Multimodal LLMs: Methods to query and interact with Large Language Models (both “”black-box”” APIs and “”white-box”” local models) without revealing the sensitive content or context of the user’s query. This must cover multimodal inputs, including text, images, and other data types.
•Privacy-Preserving Semantic Search: Protocols and algorithms for conducting secure vector-based similarity searches. This is critical for RAG and other AI applications, enabling an analyst to find relevant information without exposing their query vector to the database service.
•Confidential and Scalable PAI Collection: Methods for confidential PAI gathering that do not rely on trusted third parties, modify source data, or require collaboration with website operators.
•Robust Attribution Management and Anonymization: Advanced approaches for managing attribution and ensuring comprehensive web browsing anonymization to protect the analyst at the network level.
•Private Information Retrieval (PIR): Innovative PIR protocols to facilitate secure and private data access from public and untrusted databases.