MPLDP: Multi-Level Personalized Local Differential Privacy Method
Author:
Affiliation:
1. School of International Business, Qingdao Huanghai University, Qingdao, China
2. Department of Mathematics, Harbin Institute of Technology, Harbin, China
Funder
Second Batch of Doctoral Research Fund Project of Qingdao Huanghai University: Local Differential Privacy Optimization Method Based on Data Perturbation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/6287639/10380310/10602491.pdf?arnumber=10602491
Reference35 articles.
1. A framework for protecting worker location privacy in spatial crowdsourcing
2. Calibrating Noise to Sensitivity in Private Data Analysis
3. Presentation-level Privacy Protection Techniques for Automated Face Recognition—A Survey
4. Collecting and analyzing data from smart device users with local differential privacy;Nguyên;arXiv:1606.05053,2016
5. Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy
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