Lucene-P2: A Distributed Platform for Privacy-Preserving Text-based Search.

Area of Science:

• Computer Science
• Information Science
• Cryptography

Background:

• Information retrieval (IR) is crucial but current systems like Apache Lucene compromise user privacy.
• Existing private information retrieval (PIR) methods often fail to compute relevancy adequately.
• Searching private or sensitive data is restricted, limiting access to crucial information.

Purpose of the Study:

• To develop a private information retrieval (PIR) system that protects both user queries and database contents.
• To enable a user to determine the relevance of information within a private database without disclosure.
• To build upon existing IR technologies, such as Apache Lucene, to incorporate privacy-preserving features.

Main Methods:

• Introduction of techniques to build Lucene-P², a novel PIR system.
• Leveraging established IR software (Apache Lucene) as a foundation.
• Designing a system where information relevance can be computed without data leakage.

Main Results:

• Lucene-P² allows a party to discover relevant textual information held by a second party.
• The system ensures that neither party discloses their information (query or database content).
• Addresses the limitation of inadequate relevancy computation in previous PIR approaches.

Conclusions:

• Lucene-P² offers a viable solution for private information retrieval from sensitive datasets.
• The developed system enhances data privacy in information access.
• This research contributes to secure and private data searching capabilities.