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

Industrial Scale Privacy Preserving Deep Neural Network

no code implementations • 11 Mar 2020 • Longfei Zheng, Chaochao Chen, Yingting Liu, Bingzhe Wu, Xibin Wu, Li Wang, Lei Wang, Jun Zhou, Shuang Yang

Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.

Fraud Detection Privacy Preserving

Privacy Preserving PCA for Multiparty Modeling

no code implementations • 6 Feb 2020 • Yingting Liu, Chaochao Chen, Longfei Zheng, Li Wang, Jun Zhou, Guiquan Liu, Shuang Yang

In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data.

Fraud Detection Privacy Preserving

Federated Forest

no code implementations • 24 May 2019 • Yang Liu, Yingting Liu, Zhijie Liu, Junbo Zhang, Chuishi Meng, Yu Zheng

In this paper, we tackle these challenges and propose a privacy-preserving machine learning model, called Federated Forest, which is a lossless learning model of the traditional random forest method, i. e., achieving the same level of accuracy as the non-privacy-preserving approach.

Attribute BIG-bench Machine Learning +1