papers

Publications (7)

cs.LG2022

Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detection

Yingting Liu, Chaochao Chen, Jamie Cui +2

K-means is one of the most widely used clustering models in practice. Due to the problem of data isolation and the requirement for high model performance, how to jointly build prac…

cs.CR2023

A Customized Text Sanitization Mechanism with Differential Privacy

Huimin Chen, Fengran Mo, Yanhao Wang +4

As privacy issues are receiving increasing attention within the Natural Language Processing (NLP) community, numerous methods have been proposed to sanitize texts subject to differ…

cs.CR2022

DP-PSI: Private and Secure Set Intersection

Jian Du, Tianxi Ji, Jamie Cui +3

One way to classify private set intersection (PSI) for secure 2-party computation is whether the intersection is (a) revealed to both parties or (b) hidden from both parties while…

cs.CR2023

Online Efficient Secure Logistic Regression based on Function Secret Sharing

Jing Liu, Jamie Cui, Cen Chen

Logistic regression is an algorithm widely used for binary classification in various real-world applications such as fraud detection, medical diagnosis, and recommendation systems.…

cs.LG2021

Practical and Light-weight Secure Aggregation for Federated Submodel Learning

Jamie Cui, Cen Chen, Tiandi Ye +1

Recently, Niu, et. al. introduced a new variant of Federated Learning (FL), called Federated Submodel Learning (FSL). Different from traditional FL, each client locally trains the…

cs.DB2020

Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications

Chaochao Chen, Jamie Cui, Guanfeng Liu +2

Knowledge Graph (KG) has attracted more and more companies' attention for its ability to connect different types of data in meaningful ways and support rich data services. However,…

cs.LG2022

Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

Jamie Cui, Chaochao Chen, Lingjuan Lyu +2

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that a…