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Big Data Privacy Preservation for Cyber-Physical SystemsCaching with Users’ Differential Privacy Preservation in Information-Centric Networks

Big Data Privacy Preservation for Cyber-Physical Systems: Caching with Users’ Differential... [Information-centric networking (ICN) is developed for the future Internet because of the tremendous increase of content demands in the Internet. In the ICN architecture, in-network storage for caching plays an important role in improving content delivery efficiency, scalability and availability. To enjoy the benefits of caching users’ preferable contents without disclosing the users’ privacy, in this chapter, we aim to integrate local differential privacy (LDP) techniques into data-driven optimization, and propose a novel scheme to allow content provider (CP) to collect the locally differentially private content preferences of a selected group of users, exploit data-driven approach to predict the content popularity, and offer the cache-enabled access points (APs) economic incentives to cache the selected preferable content. Here, optimized local hashing (OLH) is employed to locally add differential private noise to the users’ preference content information and the noisy data is sent to the CP. Besides, we leverage data-driven methodology to predict the content popularity according to the constructed reference distribution of the given noisy preference content data from users. We formulate a data-driven caching revenue optimization, provide feasible solutions, and conduct simulations to show the effectiveness of the proposed scheme.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Big Data Privacy Preservation for Cyber-Physical SystemsCaching with Users’ Differential Privacy Preservation in Information-Centric Networks

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Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
ISBN
978-3-030-13369-6
Pages
47 –55
DOI
10.1007/978-3-030-13370-2_5
Publisher site
See Chapter on Publisher Site

Abstract

[Information-centric networking (ICN) is developed for the future Internet because of the tremendous increase of content demands in the Internet. In the ICN architecture, in-network storage for caching plays an important role in improving content delivery efficiency, scalability and availability. To enjoy the benefits of caching users’ preferable contents without disclosing the users’ privacy, in this chapter, we aim to integrate local differential privacy (LDP) techniques into data-driven optimization, and propose a novel scheme to allow content provider (CP) to collect the locally differentially private content preferences of a selected group of users, exploit data-driven approach to predict the content popularity, and offer the cache-enabled access points (APs) economic incentives to cache the selected preferable content. Here, optimized local hashing (OLH) is employed to locally add differential private noise to the users’ preference content information and the noisy data is sent to the CP. Besides, we leverage data-driven methodology to predict the content popularity according to the constructed reference distribution of the given noisy preference content data from users. We formulate a data-driven caching revenue optimization, provide feasible solutions, and conduct simulations to show the effectiveness of the proposed scheme.]

Published: Mar 26, 2019

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