Enhanced Multi-Party Privacy-Preserving Record Linkage Using Trusted Execution Environments

Shumin Han (), Kuixing Shen, Derong Shen and Chuang Wang
Additional contact information
Shumin Han: School of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China
Kuixing Shen: School of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China
Derong Shen: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Chuang Wang: School of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China

Mathematics, 2024, vol. 12, issue 15, 1-19

Abstract: With the world’s data volume growing exponentially, it becomes critical to link it and make decisions. Privacy-preserving record linkage (PPRL) aims to identify all the record information corresponding to the same entity from multiple data sources, without disclosing sensitive information. Previous works on multi-party PPRL methods typically adopt homomorphic encryption technology due to its ability to perform computations on encrypted data without needing to decrypt it first, thus maintaining data confidentiality. However, these methods have notable shortcomings, such as the risk of collusion among participants leading to the potential disclosure of private keys, high computational costs, and decreased efficiency. The advent of trusted execution environments (TEEs) offers a solution by protecting computations involving private data through hardware isolation, thereby eliminating reliance on trusted third parties, preventing malicious collusion, and improving efficiency. Nevertheless, TEEs are vulnerable to side-channel attacks. In this work, we propose an enhanced PPRL method based on TEE technology. Our methodology involves processing plaintext data within a TEE using the inner product mask technique, which effectively obfuscates the data, making it impervious to side-channel attacks. The experimental results demonstrate that our approach not only significantly improves resistance to side-channel attacks but also enhances efficiency, showing better performance and privacy preservation compared to existing methods. This work provides a robust solution to the challenges faced by current PPRL methods and sets the stage for future research aimed at further enhancing scalability and security.

Keywords: privacy-preserving record linkage; Paillier homomorphic encryption; inner product mask; side-channel attacks; trusted execution environments (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/15/2337/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/15/2337/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:15:p:2337-:d:1443476

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().