Data-Driven Caching With Users’ Content Preference Privacy in Information-Centric Networks
| Autor: | Xinyue Zhang, Hongning Li, Qingqi Pei, Jingyi Wang, Pan Li, Miao Pan, Yuanxiong Guo |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Applied Mathematics Preference Computer Science Applications Data-driven Backhaul (telecommunications) Information-centric networking Key (cryptography) Leverage (statistics) Differential privacy Electrical and Electronic Engineering Latency (engineering) business Computer network |
| Zdroj: | IEEE Transactions on Wireless Communications. 20:5744-5753 |
| ISSN: | 1558-2248 1536-1276 |
| Popis: | Information-centric networking (ICN) as an emerging networking paradigm has recently gained significant attention, due to the improvement of content delivery efficiency. The built-in network storage for caching is a key component in ICN to provide low latency service and reduce high backhaul traffic by caching popular content. However, users’ content preference contains individual sensitive characteristics which is distinguishable from others. Therefore, in this work, we propose a data-driven caching revenue maximization problem with the considerations of users’ local differential privacy. Specifically, we employ dBitFlip, a local differential privacy (LDP) mechanism, to locally add differential private noise to the users’ preference content information. We leverage data-driven approach to predict the content popularity based on the reference distribution constructed by the reported noisy preference content data from users, mathematically present the distance between the noisy reference distribution and the true distribution by the tolerance level, and prove the relationship among the tolerance level, differential privacy budget and the confidence level. We provide feasible solutions to the proposed revenue maximization problem, and conduct simulations to show the effectiveness of the proposed scheme. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|