Implementation and Privacy Guarantees for Scalable Keyword Search on SOLID-based Decentralized Data with Granular Visibility Constraints

• URL: http://arxiv.org/abs/2604.22100v1
• Date: Thu, 23 Apr 2026 22:16:17 GMT
• Title: Implementation and Privacy Guarantees for Scalable Keyword Search on SOLID-based Decentralized Data with Granular Visibility Constraints
• Abstract summary: ESPRESSO is a framework for scalable keyword-based search across distributed Solid pods under user-defined visibility policies.<n>It addresses key challenges of decentralized search by constructing WebID-scoped indexes within pods and employing privacy-aware metadata.<n>This paper further introduces a formal threat model for ESPRESSO, analysing the security and privacy risks associated with the generation, aggregation, and use of indexes and metadata.
• Score: 35.30802043139842
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In decentralized personal data ecosystems grounded in architectures such as Solid, users retain sovereignty over their data via personal online data stores (pods), hosted on Solid-compliant server infrastructures. In such environments, data remains under the control of pod owners, which complicates search due to distribution across numerous pods and user-specific access constraints. ESPRESSO is a decentralized framework for scalable keyword-based search across distributed Solid pods under user-defined visibility policies. It addresses key challenges of decentralized search by constructing WebID-scoped indexes within pods and employing privacy-aware metadata to enable efficient source selection and ranking across servers. This paper further introduces a formal threat model for ESPRESSO, analysing the security and privacy risks associated with the generation, aggregation, and use of indexes and metadata. These risks include unintended metadata leakage and the potential for adversaries to infer sensitive information about data that resides within personal data stores. The analysis identifies key design principles that limit metadata exposure while mitigating unauthorized inference. The proposed threat model provides a foundation for evaluating privacy-preserving decentralized search and informs the design of systems with stronger privacy guarantees.

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