Preserving Privacy Integration and Mining for Big Temporal Co-occurrence Patterns

Author:

Olawoyin Anifat M.1,Leung Carson K.1

Affiliation:

1. University of Manitoba,Department of Computer Science,Winnipeg,Canada

Funder

University of Manitoba

Publisher

IEEE

Reference41 articles.

1. Preserving Privacy of Temporal Big Data

2. Protecting the privacy of smart meter data: the differential privacy approach and the multiplicative noise approach;brackenbury;Working Paper 01 - 19 NIASRA,2019

3. Big Data Analytics of Social Network Data: Who Cares Most About You on Facebook?

4. On the 'Semantics' of Differential Privacy: A Bayesian Formulation

5. Machine Learning and OLAP on Big COVID-19 Data

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Privacy‐preserving data integration scheme in industrial robot system based on fog computing and edge computing;IET Communications;2024-03-07

2. A Privacy-Preserving Semi-Decentralized Personalized Recommendation System;2023 IEEE International Conference on Big Data (BigData);2023-12-15

3. Privacy Preservation of Big Spatio-Temporal Co-occurrence Data;2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC);2023-06

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