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Data-Driven Spectrum Trading with Secondary Users' Differential Privacy Preservation
IEEE Transactions on Dependable and Secure Computing ( IF 7.3 ) Pub Date : 2021-01-01 , DOI: 10.1109/tdsc.2019.2892447
Jingyi Wang , Xinyue Zhang , Qixun Zhang , Ming Li , Yuanxiong Guo , Zhiyong Feng , Miao Pan

Spectrum trading benefits both secondary users (SUs) and primary users (PUs), while it poses great challenges to maximize PUs’ revenue, since SUs’ demands are uncertain and individual SU's traffic portfolio contains private information. In this paper, we propose a data-driven spectrum trading scheme which maximizes PUs’ revenue and preserves SUs’ demand differential privacy. Briefly, we introduce a novel network architecture consisting of the primary service provider (PSP), the secondary service provider (SSP) and the secondary traffic estimator and database (STED). Under the proposed architecture, PSP aggregates available spectrum from PUs, and sells the spectrum to SSP at fixed wholesale price, directly to SUs at spot price, or both. The PSP has to accurately estimate SUs’ demands. To estimate SUs’ demand, the STED exploits data-driven approach to choose sampled SUs to construct the reference distribution of SUs’ demands, and utilizes reference distribution to estimate the demand distribution of all SUs. Moreover, the STED adds noises to preserve the demand differential privacy of sampled SUs before it answers the demand estimation queries from the PSP. With the estimated SUs’ demand, we formulate the revenue maximization problem into a risk-averse optimization, develop feasible solutions, and verify its effectiveness through both theoretical proof and simulations.

中文翻译:

二级用户差异化隐私保护的数据驱动频谱交易

频谱交易对二级用户(SU)和初级用户(PU)都有利,但由于SU的需求是不确定的,并且单个SU的流量组合包含私人信息,因此对PU的收入最大化提出了巨大挑战。在本文中,我们提出了一种数据驱动的频谱交易方案,该方案可以最大化 PU 的收入并保留 SU 的需求差异隐私。简而言之,我们介绍了一种由主要服务提供商 (PSP)、次要服务提供商 (SSP) 和次要流量估计器和数据库 (STED) 组成的新型网络架构。在提议的架构下,PSP 聚合来自 PU 的可用频谱,并以固定批发价格将频谱出售给 SSP,或以现货价格直接出售给 SU,或两者兼而有之。PSP 必须准确估计 SU 的需求。为了估计 SU 的需求,STED利用数据驱动的方法选择抽样的SU来构建SU需求的参考分布,并利用参考分布估计所有SU的需求分布。此外,STED 在回答来自 PSP 的需求估计查询之前,会添加噪声以保护采样 SU 的需求差异隐私。根据估计的 SU 需求,我们将收入最大化问题公式化为风险规避优化,开发可行的解决方案,并通过理论证明和模拟验证其有效性。STED 在回答来自 PSP 的需求估计查询之前,会添加噪声以保护采样 SU 的需求差异隐私。根据估计的 SU 需求,我们将收入最大化问题公式化为风险规避优化,开发可行的解决方案,并通过理论证明和模拟验证其有效性。STED 在回答来自 PSP 的需求估计查询之前,会添加噪声以保护采样 SU 的需求差异隐私。根据估计的 SU 需求,我们将收入最大化问题公式化为风险规避优化,开发可行的解决方案,并通过理论证明和模拟验证其有效性。
更新日期:2021-01-01
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