63 citations · 279 across the 28 of their papers we have counts for
4 papers · 1 filter
Differentially Private Vertical Federated Clustering
Zitao Li, Tianhao Wang, Ninghui Li
In many applications, multiple parties have private data regarding the same set of users but on disjoint sets of attributes, and a server wants to leverage the data to train a mode…
Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation
Xiaoguang Li, Ninghui Li, Wenhai Sun +2
Although local differential privacy (LDP) protects individual users' data from inference by an untrusted data curator, recent studies show that an attacker can launch a data poison…
Using Illustrations to Communicate Differential Privacy Trust Models: An Investigation of Users' Comprehension, Perception, and Data Sharing Decision
Aiping Xiong, Chuhao Wu, Tianhao Wang +4
Proper communication is key to the adoption and implementation of differential privacy (DP). However, a prior study found that laypeople did not understand the data perturbation pr…
Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models
Shagufta Mehnaz, Sayanton V. Dibbo, Ehsanul Kabir +2
Increasing use of machine learning (ML) technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to be…