Journal Article10.32604/cmc.2023.040274
A New Privacy-Preserving Data Publishing Algorithm Utilizing Connectivity-Based Outlier Factor and Mondrian Techniques
Burak Cem Kara,Can Eyüpoğlu +1 more
Abstract: ,
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Data Privacy Protection: A Novel Federated Transfer Learning Scheme for Bearing Fault Diagnosis
Lilan Liu,Zhenhao Yan,Tingting Zhang,Zenggui Gao,Hongxia Cai,Jinrui Wang +5 more
TL;DR: A novel federated transfer learning scheme is proposed for bearing fault diagnosis, addressing data island problems and protecting data privacy through distributed local model training, global model update, and differential training for enhanced domain adaptability and parameter importance ranking.
7
References
•Book
Data Mining: Concepts and Techniques
Jiawei Han,Micheline Kamber,Jian Pei +2 more
- 08 Sep 2000
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
k -anonymity: a model for protecting privacy
TL;DR: The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment and examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected.
9.2K
LOF: identifying density-based local outliers
Markus M. Breunig,Hans-Peter Kriegel,Raymond T. Ng,Jörg Sander +3 more
- 16 May 2000
TL;DR: This paper contends that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier, called the local outlier factor (LOF), and gives a detailed formal analysis showing that LOF enjoys many desirable properties.
7.3K
Differential privacy
Cynthia Dwork
- 10 Jul 2006
TL;DR: In this article, the authors give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved, and suggest a new measure, differential privacy, which, intuitively, captures the increased risk to one's privacy incurred by participating in a database.