Efficient and Secure Quantile Aggregation of Private Data Streams-Reference-Cited by-同舟云学术

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Efficient and Secure Quantile Aggregation of Private Data Streams

Published:2023 Issue: Volume:18 Page:3058-3073
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Lan Xiao¹^ORCID,Jin Hongjian²^ORCID,Guo Hui³^ORCID,Wang Xiao⁴^ORCID

Affiliation:

1. Cyber Science Research Institute, Sichuan University, Chengdu, China

2. College of Cyber Science and Engineering, Sichuan University, Chengdu, China

3. State Key Laboratory of Cryptology, Beijing, China

4. Department of Computer Science, Northwestern University, Evanston, IL, USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/10206/9970396/10115007.pdf?arnumber=10115007

Reference47 articles.

1. Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access

2. Efficient, Oblivious Data Structures for MPC

3. Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy

4. Ferret: Fast Extension for Correlated OT with Small Communication

5. Locally Differentially Private Sparse Vector Aggregation

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