Operational Privacy Preservation via Data-Driven Optimization

Jingyi Wang, Y. Gong, Ljun Qian, Riku Jäntti, Pan Miao, Zhu Han

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Recently opened spectrum within 3550-3700 MHz provides more accessing opportunities to secondary users (SUs), while it also raises concerns on the operational privacy of primary users (PUs), especially for military and government. In this paper, we propose to study the tradeoff between PUs' temporal privacy and SUs' network performance using the data-driven approach. To preserve PUs' temporal operational privacy, we develop an obfuscation strategy for PUs, which allows PUs to intentionally add dummy signals to change the distribution of temporal spectrum availability, and confuse the adversary. While generating the dummy signals for privacy, the PUs have to consider the utility of SUs and try their best to satisfy SUs' uncertain traffic demands. Based on the historical data, we employ a data-driven risk-averse model to characterize the uncertainty of SUs' demands. With joint consideration of PUs' privacy and uncertain SUs' demands, we formulate the data-driven risk- averse stochastic optimization, and provide corresponding solutions. Through numerical simulations, we show that the proposed scheme is effective in preserving PUs' temporal operational privacy while offering good enough spectrum resources to satisfy SUs' traffic demands.
Original languageEnglish
Title of host publicationGLOBECOM 2017 - 2017 IEEE Global Communications Conference
Number of pages6
ISBN (Electronic)978-1-5090-5019-2
Publication statusPublished - Dec 2017
MoE publication typeA4 Article in a conference publication
EventIEEE Global Communications Conference - Singapore, Singapore
Duration: 4 Dec 20178 Dec 2017

Publication series

NameIEEE Global Communications Conference


ConferenceIEEE Global Communications Conference
Abbreviated titleGLOBECOM


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