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DPavatar: A Real-time Location Protection Framework for Incumbent Users in Cognitive Radio Networks
IEEE Transactions on Mobile Computing ( IF 7.9 ) Pub Date : 2020-03-01 , DOI: 10.1109/tmc.2019.2897099
Jianqing Liu , Chi Zhang , Beatriz Lorenzo , Yuguang Fang

Dynamic spectrum sharing between licensed incumbent users (IUs) and unlicensed wireless industries has been well recognized as an efficient approach to solving spectrum scarcity as well as creating spectrum markets. Recently, both US and European governments called a ruling on opening up spectrum that was initially licensed to sensitive military/federal systems. However, this introduces serious concerns on operational privacy (e.g., location, time, and frequency of use) of IUs for national security concerns. Although several works have proposed obfuscation methods to address this problem, these techniques only rely on syntactic privacy models, lacking rigorous privacy guarantee. In this paper, we propose a comprehensive framework to provide real-time differential location privacy for sensitive IUs. We design a utility-optimal differentially private mechanism to reduce the loss in spectrum efficiency while protecting IUs from harmful interference. Furthermore, we strategically combine differential privacy with another privacy notion, expected inference error, to provide double shield protection for IU's location privacy. Extensive simulations are conducted to validate our design and demonstrate significant improvements in utility and location privacy compared with other existing mechanisms.

中文翻译:

DPavatar:认知无线电网络中现有用户的实时位置保护框架

授权现有用户 (IU) 和非授权无线行业之间的动态频谱共享已被公认为解决频谱稀缺和创建频谱市场的有效方法。最近,美国和欧洲政府都呼吁对开放最初授权给敏感军事/联邦系统的频谱作出裁决。然而,出于国家安全考虑,这会引起对 IU 操作隐私(例如,位置、时间和使用频率)的严重担忧。尽管一些作品提出了混淆方法来解决这个问题,但这些技术仅依赖于句法隐私模型,缺乏严格的隐私保证。在本文中,我们提出了一个综合框架,为敏感的 IU 提供实时差异位置隐私。我们设计了一种效用最优的差分私有机制,以减少频谱效率的损失,同时保护 IU 免受有害干扰。此外,我们战略性地将差分隐私与另一个隐私概念(预期推理错误)相结合,为 IU 的位置隐私提供双重屏蔽保护。进行了广泛的模拟以验证我们的设计,并展示了与其他现有机制相比在效用和位置隐私方面的显着改进。
更新日期:2020-03-01
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