To support location-based services, vehicles must share their location with a server to receive relevant data, compromising their (location) privacy. To alleviate this privacy compromise, the vehicle's location can be obfuscated by adding artificial noise. Under limited available bandwidth, and since the area including the vehicle's location increases with the noise, the server will provide fewer data relevant to the vehicle's true location, reducing the effectiveness of a location-based service. To alleviate this problem, we propose that data relevant to a vehicle is also provided through direct, ad hoc communication by neighboring vehicles. Through such Vehicle-to-Vehicle (V2V) cooperation, the impact of location obfuscation is mitigated. Since vehicles subscribe to data of (location-dependent) impact values, neighboring vehicles will subscribe to largely overlapping sets of data, reducing the benefit of V2V cooperation. To increase such benefit, we develop and study a non-cooperative game determining the data that a vehicle should subscribe to, aiming at maximizing its utilization while considering the participating (neighboring) vehicles. Our analysis and results show that the proposed V2V cooperation and derived strategy lead to significant performance increase compared to non-cooperative approaches and largely alleviates the impact of privacy on location-based services.

中文翻译：

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隐藏我：在异构车载网络中启用位置隐私

为了支持基于位置的服务，车辆必须与服务器共享其位置以接收相关数据，从而损害其（位置）隐私。为了减轻这种隐私损害，可以通过添加人工噪声来混淆车辆的位置。在有限的可用带宽下，由于包括车辆位置的区域随着噪声的增加而增加，服务器将提供较少的与车辆真实位置相关的数据，从而降低基于位置的服务的有效性。为了缓解这个问题，我们建议与车辆相关的数据也通过相邻车辆的直接、临时通信提供。通过这种车对车 (V2V) 合作，可以减轻位置混淆的影响。由于车辆订阅（位置相关）影响值的数据，相邻车辆将订阅大量重叠的数据集，从而降低 V2V 合作的收益。为了增加这种收益，我们开发并研究了一种非合作博弈，确定车辆应该订阅的数据，旨在在考虑参与（相邻）车辆的同时最大化其利用率。我们的分析和结果表明，与非合作方法相比，所提出的 V2V 合作和派生策略显着提高了性能，并在很大程度上减轻了隐私对基于位置的服务的影响。在考虑参与（相邻）车辆的同时，最大限度地提高其利用率。我们的分析和结果表明，与非合作方法相比，所提出的 V2V 合作和派生策略显着提高了性能，并在很大程度上减轻了隐私对基于位置的服务的影响。在考虑参与（相邻）车辆的同时，最大限度地提高其利用率。我们的分析和结果表明，与非合作方法相比，所提出的 V2V 合作和派生策略显着提高了性能，并在很大程度上减轻了隐私对基于位置的服务的影响。