Towards Strong Privacy Protection for Association Rule Mining and Query in the Cloud-Reference-Cited by-同舟云学术

Towards Strong Privacy Protection for Association Rule Mining and Query in the Cloud

Published:2023 Issue: Volume: Page:1-17
ISSN:2168-7161
Container-title:IEEE Transactions on Cloud Computing
language:
Short-container-title:IEEE Trans. Cloud Comput.

Author:

Liu Lin¹^ORCID,Su Jinshu¹^ORCID,Liu Ximeng²^ORCID,Chen Rongmao¹^ORCID,Huang Xinyi³^ORCID,Kou Guang⁴,Fu Shaojing¹^ORCID

Affiliation:

1. College of Computer, National University of Defense Technology, Changsha, Hunan, China

2. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian, China

3. Thrust of Artificial Intelligence, Information Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong Province, China

4. Artificial Intelligence Research Center, National Innovation Institute of Defense Technology, Beijing, China

Funder

National Nature Science Foundation of China

Natural Science Foundation of Hunan Province

NUDT

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Software

Link

http://xplorestaging.ieee.org/ielx7/6245519/6562694/10111052.pdf?arnumber=10111052

Reference46 articles.

1. Privacy preserving association rule mining in vertically partitioned data

2. Private Approximate Nearest Neighbor Search with Sublinear Communication

3. Privacy-preserving distributed mining of association rules on horizontally partitioned data

4. Secure and Efficient Outsourced k-Means Clustering using Fully Homomorphic Encryption With Ciphertext Packing Technique

5. Secure Outsourced Frequent Pattern Mining by Fully Homomorphic Encryption

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