Re-Identification in Differentially Private Incomplete Datasets-Reference-Cited by-同舟云学术

Re-Identification in Differentially Private Incomplete Datasets

Published:2022 Issue: Volume:3 Page:62-72
ISSN:2644-1268
Container-title:IEEE Open Journal of the Computer Society
language:
Short-container-title:IEEE Open J. Comput. Soc.

Author:

Sei Yuichi¹^ORCID,Okumura Hiroshi²,Ohsuga Akihiko¹^ORCID

Affiliation:

1. University of Electro-Communications, Tokyo, Japan

2. Mitsubishi Research Institute, Tokyo, Japan

Funder

JSPS KAKENHI

JST, PRESTO

The University of Electro-Communications

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Industrial and Manufacturing Engineering,Metals and Alloys,Strategy and Management,Mechanical Engineering

Link

http://xplorestaging.ieee.org/ielx7/8782664/9682503/09779455.pdf?arnumber=9779455

Reference42 articles.

1. k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY

2. Local differential privacy for sampling;husain;Proc Int Conf Artif Intell Statist,2020

3. DP-MERF: Differentially private mean embeddings with randomfeatures for practical Privacy-preserving data generation;harder;Proc Int Conf Artif Intell Statist,2021

4. Data Level Privacy Preserving: A Stochastic Perturbation Approach based on Differential Privacy

5. Research on an Ensemble Classification Algorithm Based on Differential Privacy

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4. Machine Learning Model Generation With Copula-Based Synthetic Dataset for Local Differentially Private Numerical Data;IEEE Access;2022

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