Abstract
There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance. However, knowledge transfer is difficult because of restrictions that are in place to ensure data security and privacy. Institutions are not allowed to exchange data or perform any activity that may expose personal information. With the leverage of a differential privacy algorithm in a high-performance computing environment, we propose a new training protocol, Bootstrap Aggregation of Teacher Ensembles (BATE), which is applicable to various types of machine learning models. The BATE algorithm is based on and provides enhancements to the PATE algorithm, maintaining competitive task performance scores on complex datasets with underrepresented class labels. We conducted a proof-of-the-concept study of the information extraction from cancer pathology report data from four cancer registries and performed comparisons between four scenarios: no collaboration, no privacy-preserving collaboration, the PATE algorithm, and the proposed BATE algorithm. The results showed that the BATE algorithm maintained competitive macro-averaged F1 scores, demonstrating that the suggested algorithm is an effective yet privacy-preserving method for machine learning and deep learning solutions.
Original language | English |
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Title of host publication | Heterogeneous Data Management, Polystores, and Analytics for Healthcare - VLDB Workshops, Poly 2020 and DMAH 2020, Revised Selected Papers |
Editors | Vijay Gadepally, Timothy Mattson, Michael Stonebraker, Tim Kraska, Fusheng Wang, Gang Luo, Jun Kong, Alevtina Dubovitskaya |
Pages | 87-99 |
Number of pages | 13 |
DOIs | |
State | Published - 2021 |
Event | VLDB workshops: International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2020, and 6th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2020 - Virtual, Online Duration: Aug 31 2020 → Sep 4 2020 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12633 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | VLDB workshops: International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2020, and 6th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2020 |
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City | Virtual, Online |
Period | 8/31/20 → 9/4/20 |
Bibliographical note
Publisher Copyright:© 2021, Springer Nature Switzerland AG.
Keywords
- Bootstrap aggregation
- Data privacy
- Differential privacy
- Information extraction
- Natural language processing
- Privacy-preserving machine learning
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science