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

no code implementations • 12 Aug 2022 • Yingting Liu, Chaochao Chen, Jamie Cui, Li Wang, Lei Wang

The second type is provable secure but is inefficient and even helpless for the large-scale data sparsity scenario.

Clustering Fraud Detection +1

Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

no code implementations • NeurIPS 2021 • Jamie Cui, Chaochao Chen, Lingjuan Lyu, Carl Yang, Li Wang

As a result, our model can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms.

Information Retrieval Retrieval

Practical and Light-weight Secure Aggregation for Federated Submodel Learning

no code implementations • 2 Nov 2021 • Jamie Cui, Cen Chen, Tiandi Ye, Li Wang

Existing work fails to provide a loss-less scheme, or has impractical efficiency.

Federated Learning Privacy Preserving +1

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

no code implementations • 20 Nov 2020 • Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, Li Wang

In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in data isolation setting and propose possible solutions for them.

Privacy Preserving