Differential Privacy for Class-Based Data: A Practical Gaussian Mechanism-Reference-Cited by-同舟云学术

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Differential Privacy for Class-Based Data: A Practical Gaussian Mechanism

Published:2023 Issue: Volume:18 Page:5096-5108
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Ramakrishna Raksha¹^ORCID,Scaglione Anna²^ORCID,Wu Tong²^ORCID,Ravi Nikhil²^ORCID,Peisert Sean³^ORCID

Affiliation:

1. Division of Network and Systems Engineering, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden

2. Department of Electrical and Computer Engineering, Cornell Tech, New York City, NY, USA

3. Computing Sciences Research, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Funder

U.S. Department of Energy through the Cybersecurity, Energy Security, and Emergency Response Program, and the Cybersecurity for Energy Delivery Systems Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

https://ieeexplore.ieee.org/ielam/10206/9970396/10160035-aam.pdf

Reference34 articles.

1. OptStream: Releasing Time Series Privately

2. Pufferfish Privacy Mechanisms for Correlated Data

3. Differentially Private K-Means Clustering Applied to Meter Data Analysis and Synthesis

4. Old techniques in differentially private linear regression;sheffet;Proc Algorithmic Learn Theory,2019

5. Differential Privacy for Dynamic Data

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