A conditional privacy-preserving fair electronic payment scheme based on blockchain without trusted third party

2022 ◽  
Author(s):  
Zahra Hatefi ◽  
Majid Bayat ◽  
Mahdi R. Alaghband ◽  
Negin Hamian ◽  
Seyed Morteza Pournaghi
Keyword(s):  
Privacy Preserving ◽  
Third Party ◽  
Electronic Payment ◽  
Trusted Third Party ◽  
Payment Scheme

scholarly journals Mainzelliste SecureEpiLinker (MainSEL): Privacy-Preserving Record Linkage using Secure Multi-Party Computation

2020 ◽  
Author(s):  
Sebastian Stammler ◽  
Tobias Kussel ◽  
Phillipp Schoppmann ◽  
Florian Stampe ◽  
Galina Tremper ◽  
...  
Keyword(s):  
Record Linkage ◽  
Fault Tolerant ◽  
Source Code ◽  
Privacy Preserving ◽  
Third Party ◽  
Data Sets ◽  
Real World Data ◽  
Trusted Third Party ◽  
Record Keeping ◽  
Network Connection

Abstract Motivation Record Linkage has versatile applications in real-world data analysis contexts, where several data sets need to be linked on the record level in the absence of any exact identifier connecting related records. An example are medical databases of patients, spread across institutions, that have to be linked on personally identifiable entries like name, date of birth or ZIP code. At the same time, privacy laws may prohibit the exchange of this personally identifiable information (PII) across institutional boundaries, ruling out the outsourcing of the record linkage task to a trusted third party. We propose to employ privacy-preserving record linkage (PPRL) techniques that prevent, to various degrees, the leakage of PII while still allowing for the linkage of related records. Results We develop a framework for fault-tolerant PPRL using secure multi-party computation with the medical record keeping software Mainzelliste as the data source. Our solution does not rely on any trusted third party and all PII is guaranteed to not leak under common cryptographic security assumptions. Benchmarks show the feasibility of our approach in realistic networking settings: linkage of a patient record against a database of 10.000 records can be done in 48s over a heavily delayed (100ms) network connection, or 3.9s with a low-latency connection. Availability and implementation The source code of the sMPC node is freely available on Github at https://github.com/medicalinformatics/SecureEpilinker subject to the AGPLv3 license. The source code of the modified Mainzelliste is available at https://github.com/medicalinformatics/MainzellisteSEL.

scholarly journals Federated Trusted Third Party as an Approach for Privacy Preserving Record Linkage in a Large Network of University Medicines in Pandemic Research

2021 ◽  
Author(s):  
Christopher Hampf ◽  
Martin Bialke ◽  
Hauke Hund ◽  
Christian Fegeler ◽  
Stefan Lang ◽  
...  
Keyword(s):  
Data Integration ◽  
Record Linkage ◽  
Privacy Preserving ◽  
Medical Data ◽  
Third Party ◽  
Bloom Filters ◽  
Large Network ◽  
Trusted Third Party ◽  
University Medicine ◽  
Expansion Stage

Abstract BackgroundThe Federal Ministry of Research and Education funded the Network of University Medicine for establishing an infrastructure for pandemic research. This includes the development of a COVID-19 Data Exchange Platform (CODEX) that provides standardised and harmonised data sets for COVID-19 research. Nearly all university hospitals in Germany are part of the project and transmit medical data from the local data integration centres to the CODEX platform. The medical data on a person that has been collected at several sites is to be made available on the CODEX platform in a merged form. To enable this, a federated trusted third party (fTTP) will be established, which will allow the pseudonymised merging of the medical data. The fTTP implements privacy preserving record linkage based on Bloom filters and assigns pseudonyms to enable re-pseudonymisation during data transfer to the CODEX platform.ResultsThe fTTP was implemented conceptually and technically. For this purpose, the processes that are necessary for data delivery were modelled. The resulting communication relationships were identified and corresponding interfaces were specified. These were developed according to the specifications in FHIR and validated with the help of external partners. Existing tools such as the identity management system E-PIX® were further developed accordingly so that sites can generate Bloom filters based on person identifying information. An extension for the comparison of Bloom filters was implemented for the federated trust third party. The correct implementation was shown in the form of a demonstrator and the connection of two data integration centres.ConclusionsThis article describes how the fTTP was modelled and implemented. In a first expansion stage, the fTTP was exemplarily connected through two sites and its functionality was demonstrated. Further expansion stages, which are already planned, have been technically specified and will be implemented in the future in order to also handle cases in which the privacy preserving record linkage achieves ambiguous results. The first expansion stage of the fTTP is available in the University Medicine network and will be connected by all participating sites in the ongoing test phase.

scholarly journals Privacy-Preserving K-Nearest Neighbors Training over Blockchain-Based Encrypted Health Data

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2096
Author(s):  
Rakib Ul Haque ◽  
A S M Touhidul Hasan ◽  
Qingshan Jiang ◽  
Qiang Qu
Keyword(s):  
Data Privacy ◽  
Building Blocks ◽  
Privacy Preserving ◽  
Training Data ◽  
Third Party ◽  
Supervised Machine Learning ◽  
Primary Concern ◽  
Privacy Issue ◽  
Trusted Third Party ◽  
Blockchain Technology

Numerous works focus on the data privacy issue of the Internet of Things (IoT) when training a supervised Machine Learning (ML) classifier. Most of the existing solutions assume that the classifier’s training data can be obtained securely from different IoT data providers. The primary concern is data privacy when training a K-Nearest Neighbour (K-NN) classifier with IoT data from various entities. This paper proposes secure K-NN, which provides a privacy-preserving K-NN training over IoT data. It employs Blockchain technology with a partial homomorphic cryptosystem (PHC) known as Paillier in order to protect all participants (i.e., IoT data analyst C and IoT data provider P) data privacy. When C analyzes the IoT data of P, both participants’ privacy issue arises and requires a trusted third party. To protect each candidate’s privacy and remove the dependency on a third-party, we assemble secure building blocks in secure K-NN based on Blockchain technology. Firstly, a protected data-sharing platform is developed among various P, where encrypted IoT data is registered on a shared ledger. Secondly, the secure polynomial operation (SPO), secure biasing operations (SBO), and secure comparison (SC) are designed using the homomorphic property of Paillier. It shows that secure K-NN does not need any trusted third-party at the time of interaction, and rigorous security analysis demonstrates that secure K-NN protects sensitive data privacy for each P and C. The secure K-NN achieved 97.84%, 82.33%, and 76.33% precisions on BCWD, HDD, and DD datasets. The performance of secure K-NN is precisely similar to the general K-NN and outperforms all the previous state of art methods.

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