Blockchain-based Privacy-Preserving Record Linkage: enhancing data privacy in an untrusted environment. Issue 102 (December 2021)
- Record Type:
- Journal Article
- Title:
- Blockchain-based Privacy-Preserving Record Linkage: enhancing data privacy in an untrusted environment. Issue 102 (December 2021)
- Main Title:
- Blockchain-based Privacy-Preserving Record Linkage: enhancing data privacy in an untrusted environment
- Authors:
- Nóbrega, Thiago
Pires, Carlos Eduardo S.
Nascimento, Dimas Cassimiro - Abstract:
- Abstract: Privacy-Preserving Record Linkage (PPRL) intends to integrate private data from several data sources held by different parties. Due to recent laws and regulations (e.g, General Data Protection Regulation), PPRL approaches are increasingly demanded in real-world application areas such as health-care, credit analysis, public policy evaluation, and national security. However, the majority of the PPRL approaches consider an unrealistic adversary model, particularly the Honest but Curious (HBC) model, which assumes that all PPRL parties will follow a pre-agreed data integration protocol, and will not try to break the confidentiality of the data handled during the process. The HBC model is hard to employ in real-world applications, mainly because of the need to trust other parties fully. To overcome the limitations associated with the majority of the adversary models considered by PPRL approaches, we propose a protocol that considers covert adversaries, i.e., adversaries that may deviate arbitrarily from the protocol specification in an attempt to cheat. In such protocol, however, the honest parties are able to detect this misbehavior with a high probability. To provide a proof-of-concept implementation of this protocol, we employ the Blockchain technology and propose an improvement in the most used anonymization technique for PPRL, the Bloom Filter. The evaluation carried out using several real-world data sources has demonstrated the effectiveness (linkage quality)Abstract: Privacy-Preserving Record Linkage (PPRL) intends to integrate private data from several data sources held by different parties. Due to recent laws and regulations (e.g, General Data Protection Regulation), PPRL approaches are increasingly demanded in real-world application areas such as health-care, credit analysis, public policy evaluation, and national security. However, the majority of the PPRL approaches consider an unrealistic adversary model, particularly the Honest but Curious (HBC) model, which assumes that all PPRL parties will follow a pre-agreed data integration protocol, and will not try to break the confidentiality of the data handled during the process. The HBC model is hard to employ in real-world applications, mainly because of the need to trust other parties fully. To overcome the limitations associated with the majority of the adversary models considered by PPRL approaches, we propose a protocol that considers covert adversaries, i.e., adversaries that may deviate arbitrarily from the protocol specification in an attempt to cheat. In such protocol, however, the honest parties are able to detect this misbehavior with a high probability. To provide a proof-of-concept implementation of this protocol, we employ the Blockchain technology and propose an improvement in the most used anonymization technique for PPRL, the Bloom Filter. The evaluation carried out using several real-world data sources has demonstrated the effectiveness (linkage quality) obtained by our contributions, as well as the ability to detect the misbehavior of a malicious adversary during the PPRL execution. Highlights: Blockchain networks can be employed to audit Privacy-Preserving Record Linkage. Bloom Filter is divided into multiples splits to reduce the amount of information available. The auditability of the PPRL process reduces the trust needed by the PPRL parties. … (more)
- Is Part Of:
- Information systems. Issue 102(2021)
- Journal:
- Information systems
- Issue:
- Issue 102(2021)
- Issue Display:
- Volume 102, Issue 102 (2021)
- Year:
- 2021
- Volume:
- 102
- Issue:
- 102
- Issue Sort Value:
- 2021-0102-0102-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-12
- Subjects:
- Entity resolution -- Privacy preserving entity resolution -- Data privacy -- Bloom Filter -- Blockchain
Database management -- Periodicals
Electronic data processing -- Periodicals
Bases de données -- Gestion -- Périodiques
Informatique -- Périodiques
Database management
Electronic data processing
Periodicals
005.7 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03064379 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.is.2021.101826 ↗
- Languages:
- English
- ISSNs:
- 0306-4379
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4496.367300
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 18757.xml