Outsourced and Practical Privacy-Preserving K-Prototype Clustering supporting Mixed Data-Reference-Cited by-同舟云学术

Outsourced and Practical Privacy-Preserving K-Prototype Clustering supporting Mixed Data

Published:2022-05-16 Issue: Volume: Page:
ISSN:
Container-title:ICC 2022 - IEEE International Conference on Communications
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Author:

Bi Renwan¹,Xiong Jinbo¹,Lin Jie¹,Zhao Min²,Tian Youliang²

Affiliation:

1. Fujian Normal University,Fujian Provincial Key Laboratory of Network System Information Security, College of Computer and Cyber Security,Fuzhou,China,350117

2. Guizhou University,College of Computer Science and Technology,Guiyang,China,550025

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9837954/9838246/09838328.pdf?arnumber=9838328

Reference20 articles.

1. Toward Lightweight, Privacy-Preserving Cooperative Object Classification for Connected Autonomous Vehicles

2. Efficient and Secure Decision Tree Classification for Cloud-Assisted Online Diagnosis Services

3. Privacy-preserving distributed k-means clustering over arbitrarily partitioned data

4. Mutual Privacy Preserving $k$ -Means Clustering in Social Participatory Sensing

5. Privacy of outsourced k-means clustering

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1. LSTN: A Lightweight Secure Three-Party Inference Framework for Deep Neural Networks;ICC 2024 - IEEE International Conference on Communications;2024-06-09

2. Outsourced and Privacy-Preserving Collaborative k-Prototype Clustering for Mixed Data via Additive Secret Sharing;IEEE Internet of Things Journal;2023-09-15

3. Achieving Lightweight and Privacy-Preserving Object Detection for Connected Autonomous Vehicles;IEEE Internet of Things Journal;2023-02-01

4. Edge-Cooperative Privacy-Preserving Object Detection Over Random Point Cloud Shares for Connected Autonomous Vehicles;IEEE Transactions on Intelligent Transportation Systems;2022-12