Positional Information Leakage of Wireless Consumer Electronic Devices

智能手机和其他便携式设备不断发射信号，与Wi-fi、蓝牙和4G/5G进行通信。在过去的工作中，这些排放物已经被证明可以揭示用户的位置和身份。这带来了隐私风险，因为企业出于营销和分析目的或攻击者出于更恶意的原因可以长期跟踪用户的移动。当前的对策包括探测wi-fi网络时的MAC地址随机化，或在移动位置时手动更改MAC地址。然而，当使用信号指纹方法来唯一地识别设备而不考虑传输的数据内容时，这可能不足以防止识别。我的研究旨在探索与使用商用现货（COTS）硬件的消费电子设备的身份信息泄漏和细粒度本地化相关的问题。我主要关注的是利用智能手机天线的辐射模式来了解更多关于设备的信息，而不仅仅是一个粗略的位置。智能手机的天线模式可用于识别基于独特信号强度模式的设备，并确定其方向，并通过将当前信号强度测量值映射到先前测量的天线模式来了解更多关于其细颗粒运动的信息。这些攻击方法还有待探索，并造成严重的隐私问题，使现有的跟踪对策无效，我将寻求开发一系列新的对策来防止这种隐私风险。Wi-fi发射将成为焦点，因为传输功率相对较高，这使得它们比蓝牙在更远的距离上可测量，并且与LTE和5G不同，使用廉价的COTS硬件和开源软件非常容易收集和处理。不安全的物联网（IoT）设备带来了特别的风险，因为它们提供了一个可能的平台来进行这类攻击。虽然存在明显的风险，但也有提高安全性的潜在合法用途，例如检测未经授权的设备/人员入侵安全空间。该项目属于EPSRC数字经济研究领域和安全、隐私和信任子主题。

Smartphones and other portable devices continuously emit signals for communication with Wi-fi, Bluetooth, and 4G/5G. In the past work these emissions have been shown to reveal the location and identity of users. This presents privacy risks as a user's movement can be tracked overtime by business for marketing and analytics purposes or attackers for more malicious reasons. Current countermeasures include MAC address randomisation when probing for wi-fi networks or manually changing MAC addresses when moving between locations. However, this may not be sufficient to prevent identification when signal fingerprinting methods are used to uniquely identify a device without considering the data content of the transmission.My research aims to explore issues related to identity information leakage and fine grained localisation using consumer electronic devices using commercial-off-the-shelf (COTS) hardware. My main focus is on the potential to exploit the radiation pattern of smartphone antennae to learn more about a device than simply a rough location. The antenna patterns of smartphones could be used to identify devices based on unique signal strength patterns and determine their orientation and learn more about their fine grain movement by mapping current signal strength measurements to previously measured antenna patterns. These attack methods are yet to be explored and pose severe privacy issues rendering existing tracking countermeasures ineffective and I will look to develop a new range of countermeasures to protect against this privacy risks. Wi-fi emissions will be the focal point as the power of transmissions are relatively high which makes them measurable over larger distances than Bluetooth and are very easy to collect and process using cheap COTS hardware and opensource software unlike LTE and 5G. Insecure Internet of Things (IoT) devices present a particular risk because they provide a possible platform from which to conduct these kinds of attacks. Although there are clear risks, there are a potentially legitimate uses for improving security, such as intrusion detection of unauthorised devices/people into secure spaces.This project falls within the EPSRC Digital economy research area and the Security, privacy, and trust sub theme.