# BECKON - Block Estimate Chain: creating Knowledge ON demand & protecting privacy

> **NIH NIH R00** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $102,049

## Abstract

Project Summary/Abstract
Artificial intelligence on genomic/healthcare data that is performed jointly between multiple collaborating
institutions relies on a trust model but can accelerate genomic medicine research and facilitate quality
improvement. To conduct such machine learning while protecting patient privacy and reducing security risks, we
are developing blockchain-based privacy-preserving learning methods in a K99/R00 study supported by the
National Human Genome Research Institute (NHGRI). However, our previous design of privacy-preserving
learning on private blockchain assumed “semi-honesty” as the underlying adversary assumption. That is, we
assume that each participating site is curious yet very careful and honest, such that it would only submit correct
predictive models. Nevertheless, in real world this assumption may be too optimistic; the models submitted could
be an old one due to network latency or malicious users may try to create fake models, which can in turn lead to
bioethical concerns and reduce the incentives for genomic/clinical institutions to participate in the collaborative
predictive modeling. Therefore, the capability to detect, assess and prevent “model misconducts” is critical to
increase the integrity/reliability of machine learning.
To address this issue, we consider the following 3 types of model misconducts: (1) model plagiarism, of which a
site becomes a free-rider and just submits a copy of a model from the other sites, trying to hide their own
information and inspect models from other sites; (2) model fabrication, of which a site mocks up a model, trying
to hide information and disturb the machine learning process; and (3) model falsification, of which a site tweaks
its model a bit, trying to just disturb the learning process. For each type of the model misconducts, we are interest
in how to detect these misconducts of another site, how to assess the losses of machine learning results due to
misconducts, and how to prevent these model misconducts. Our aims include (a) detecting model misconducts
using model properties, (b) assessing model misconducts losses via model simulation, and (c) preventing model
misconducts based on whole model history. The innovative components to our proposed project include (i)
summarizing various types of model misconduct, (ii) developing a complete strategy to handle the model
misconduct, and (iii) providing a generalizable approach to mitigate bioethical concerns for collaborative machine
learning.

## Key facts

- **NIH application ID:** 10130868
- **Project number:** 3R00HG009680-04S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Tsung-Ting Kuo
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $102,049
- **Award type:** 3
- **Project period:** 2019-05-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10130868

## Citation

> US National Institutes of Health, RePORTER application 10130868, BECKON - Block Estimate Chain: creating Knowledge ON demand & protecting privacy (3R00HG009680-04S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10130868. Licensed CC0.

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