Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis

By Jing Ma, Qiuchen Zhang, Jian Lou, Joyce Ho, Li Xiong, Xiaoqian Jiang. 2019

55 citations recorded by the source corpus.

Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meaningful clinical concepts. While distributing the tensor factorization tasks to local sites can avoid direct data sharing, it still requires the exchange of intermediary results which could reveal sensitive patient information. Therefore, the challenge is how to jointly decompose the tensor under rigorous and principled privacy constraints, while still support the model's interpretability. We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR. It embeds advanced privacy-preserving mechanisms with collaborative learning. Hospitals can keep their EHR database private but also collaboratively learn meaningful clinical concepts by sharing differentially private intermediary results. Moreover, DPFact solves the heterogeneous patient population using a structured sparsity term. In our framework, each hospital decomposes its local tensors and sends the updated intermediary results with output perturbation every several iterations to a semi-trusted server which generates the phenotypes. The evaluation on both real-world and synthetic datasets demonstrated that under strict privacy constraints, our method is more accurate and communication-efficient than state-of-the-art baseline methods.

Subfields: Artificial Intelligence, Computational Mathematics, Privacy & Security, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Martin Jaggi
2. Qiang Yang
3. Jakub Konecný
4. Peter Kairouz
5. Peter Richtárik
6. Ananda Theertha Suresh
7. Jianyu Wang
8. Sebastian U. Stich

In Computational Mathematics

1. Felix X. Yu
2. Qiang Yang
3. Martin Jaggi
4. Mehryar Mohri

In Privacy & Security

1. Peter Kairouz
2. Badih Ghazi
3. Rasmus Pagh
4. Qiang Yang
5. Ziteng Sun
6. Adrià Gascón
7. H. Brendan McMahan
8. Aurélien Bellet

In Medical & Health AI

1. Arne Küderle
2. Rodolfo Stoffel Antunes
3. Cristiano André da Costa
4. Qiang Yang
5. B. Eskofier
6. Ben Hutchinson
7. Imrana Abdullahi Yari
8. Yang Liu

Citations in the corpus

• Advances and Open Problems in Federated Learning (2019, methodology): “Among the existing works, [236] used an alternating direction method of multipliers (ADMM) based approach and [280] improved the efficiency with the elastic averaging SGD (EASGD) algorithm and further ensures differential privacy for the intermediate factors.”
• Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, background): “[78] focus on the computational phenotyping of EHR datasets to identify classiications of each health record.”