Privacy-Preserving Data Management using Blockchains

TL;DR

This work tackles data-provider privacy in large-scale data management by introducing a blockchain-based framework that tightly couples private attribute data, privacy preferences, and data-accessor profiles into a privacy tuple. The privacy tuple is hashed and stored on a private blockchain while raw data remain in a relational database, enabling tamper-resistant policy enforcement and secure query processing. A formal contextualized privacy ontology underpins the model, supporting dynamic policy updates and cross-domain adaptability. Empirical evaluation shows efficient privacy-aware query processing with measurable overheads, and the approach is contrasted with prior blockchain privacy methods to highlight its explicit data-element coupling and infrastructure integration. The method offers a practical, auditable privacy-preserving platform with clear pathways for future work on join queries and broader deployments.

Abstract

Privacy-preservation policies are guidelines formulated to protect data providers private data. Previous privacy-preservation methodologies have addressed privacy in which data are permanently stored in repositories and disconnected from changing data provider privacy preferences. This occurrence becomes evident as data moves to another data repository. Hence, the need for data providers to control and flexibly update their existing privacy preferences due to changing data usage continues to remain a problem. This paper proposes a blockchain-based methodology for preserving data providers private and sensitive data. The research proposes to tightly couple data providers private attribute data element to privacy preferences and data accessor data element into a privacy tuple. The implementation presents a framework of tightly-coupled relational database and blockchains. This delivers secure, tamper-resistant, and query-efficient platform for data management and query processing. The evaluation analysis from the implementation validates efficient query processing of privacy-aware queries on the privacy infrastructure.

Figures (13)

• Figure 1: System Methodology Overview and Architecture
• Figure 2: Formal Contextualized Privacy Ontology Model
• Figure 3: Attribute Data Privacy Preferences Ontology Model
• Figure 4: Tight-coupling of Data Elements into Privacy Tuple
• Figure 5: Architecture of Tight-Coupling of Relational Database and Blockchains