no code implementations • 31 Jan 2024 • Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang
In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated the performance of the synthetically generated notes in a clinical domain task, and (iv) proposed a way to mount a membership inference attack where the target model is trained with synthetic data.
no code implementations • 28 Sep 2021 • Wentao Li, Jiayi Tong, Md. Monowar Anjum, Noman Mohammed, Yong Chen, Xiaoqian Jiang
Objectives: This paper develops two algorithms to achieve federated generalized linear mixed effect models (GLMM), and compares the developed model's outcomes with each other, as well as that from the standard R package (`lme4').
no code implementations • 18 Aug 2021 • Md Monowar Anjum, Noman Mohammed, Xiaoqian Jiang
In this work, we propose a novel problem formulation for de-identification of unstructured clinical text.
1 code implementation • 7 Mar 2017 • Md Nazmus Sadat, Md Momin Al Aziz, Noman Mohammed, Feng Chen, Shuang Wang, Xiaoqian Jiang
In this article, we present SAFETY, a hybrid framework, which can securely perform GWAS on federated genomic datasets using homomorphic encryption and recently introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure high efficiency and privacy at the same time.
Cryptography and Security
no code implementations • 22 Dec 2015 • Baichuan Zhang, Noman Mohammed, Vachik Dave, Mohammad Al Hasan
Over the last decade, proliferation of various online platforms and their increasing adoption by billions of users have heightened the privacy risk of a user enormously.