Virtual reality (VR) telepresence applications and the so-called "metaverse" promise to be the next major medium of interaction with the internet. However, with numerous recent studies showing the ease at which VR users can be profiled, deanonymized, and data harvested, metaverse platforms carry all the privacy risks of the current internet and more while at present having none of the defensive privacy tools we are accustomed to using on the web. To remedy this, we present the first known method of implementing an "incognito mode" for VR. Our technique leverages local differential privacy to quantifiably obscure sensitive user data attributes, with a focus on intelligently adding noise when and where it is needed most to maximize privacy while minimizing usability impact. Moreover, our system is capable of flexibly adapting to the unique needs of each metaverse application to further optimize this trade-off. We implement our solution as a universal Unity (C#) plugin that we then evaluate using several popular VR applications. Upon faithfully replicating the most well-known VR privacy attack studies, we show a significant degradation of attacker capabilities when using our proposed solution.
翻译:虚拟现实( VR) 远程现场应用和所谓的“ 元数据” 承诺将是与互联网互动的下一个主要媒介。 然而,最近许多研究表明, VR 用户很容易被剖析、去匿名和数据采集,而元数据平台则包含当前互联网的所有隐私风险,而目前没有我们在网络上习惯使用的防御性隐私工具。为了纠正这一点,我们提出了第一个已知的为 VR 实施“ 隐形模式” 的方法。我们的技术利用本地差异隐私进行可量化的隐蔽敏感用户数据属性,重点是在最需要时和最需要的地方明智地增加噪音,最大限度地增加隐私,同时尽量减少可使用性的影响。此外,我们的系统能够灵活地适应每个逆向应用的独特需求,以进一步优化这种交易。我们实施我们的解决方案,作为一个普遍统一( C# ) 插件,然后我们用几个广受欢迎的 VR 应用程序进行评估。我们忠实地复制了最著名的 VR 隐私攻击研究,在使用拟议解决方案时显示攻击者能力显著退化。