Device fingerprinting technologies are widely employed in smartphones. However, the features used in existing schemes may bring the privacy disclosure problems because of their fixed and invariable nature (such as IMEI and OS version), or the draconian of their experimental conditions may lead to a large reduction in practicality. Finding a new, secure, and effective smartphone fingerprint is, however, a surprisingly challenging task due to the restrictions on technology and mobile phone manufacturers. To tackle this challenge, we propose a battery-based fingerprinting method, named PowerPrint, which captures the feature of power consumption rather than invariable information of the battery. Furthermore, power consumption information can be easily obtained without strict conditions. We design an unsupervised learning-based algorithm to fingerprint the battery, which is stimulated with different power consumption of tasks to improve the performance. We use 15 smartphones to evaluate the performance of PowerPrint in both laboratory and public conditions. The experimental results indicate that battery fingerprint can be efficiently used to identify smartphones with low overhead. At the same time, it will not bring privacy problems, since the power consumption information is changing in real time.

中文翻译：

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PowerPrint：通过电池电量识别智能手机

设备指纹技术已广泛应用于智能手机中。但是，现有方案中使用的功能由于其固定和不变的性质（例如IMEI和OS版本）而可能带来隐私公开问题，或者其试验条件的苛刻性可能导致实用性大大降低。然而，由于技术和手机制造商的限制，寻找一种新的，安全有效的智能手机指纹是一项令人惊讶的挑战性任务。为了解决这一挑战，我们提出了一种基于电池的指纹识别方法，称为PowerPrint，该方法可以捕获功耗特征，而不是捕获电池的不变信息。此外，无需严格的条件就可以容易地获得功耗信息。我们设计了一种无监督的基于学习的算法来对电池进行指纹识别，并通过不同的任务功耗对其进行刺激以提高性能。我们使用15部智能手机评估PowerPrint在实验室和公共条件下的性能。实验结果表明，电池指纹可以有效地用于识别开销较低的智能手机。同时，由于功耗信息是实时变化的，因此不会带来隐私问题。