Power side channel is a very important category of side channels, which can be exploited to steal confidential information from a computing system by analyzing its power consumption. In this paper, we demonstrate the existence of various power side channels on popular mobile devices such as smartphones. Based on unprivileged power consumption traces, we present a list of real-world attacks that can be initiated to identify running apps, infer sensitive UIs, guess password lengths, and estimate geo-locations. These attack examples demonstrate that power consumption traces can be used as a practical side channel to gain various confidential information of mobile apps running on smartphones. Based on these power side channels, we discuss possible exploitations and present a general approach to exploit a power side channel on an Android smartphone, which demonstrates that power side channels pose imminent threats to the security and privacy of mobile users. We also discuss possible countermeasures to mitigate the threats of power side channels.
Chari @cite_43 propose a sound approach to counteract power analysis attacks. It includes an abstract model which approximates power consumption in most devices and a generic technique to create provably resistant implementations for devices where the power model has reasonable properties. They prove a lower bound on the number of experiments required to mount statistical attacks on devices whose physical characteristics satisfy reasonable properties.
@cite_43
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Side channel cryptanalysis techniques such as the analysis of instantaneous power consumption, have been extremely effective in attacking implementations on simple hardware platforms. There are several proposed solutions to resist these attacks, most of which are ad-hoc and can easily be rendered ineffective. A scientific approach is to create a model for the physical characteristics of the device, and then design implementations provably secure in that model, i.e, they resist generic attacks with an a priori bound on the number of experiments. We propose an abstract model which approximates power consumption in most devices and in particular small single-chip devices. Using this, we propose a generic technique to create provably resistant implementations for devices where the power model has reasonable properties, and a source of randomness exists. We prove a lower bound on the number of experiments required to mount statistical attacks on devices whose physical characteristics satisfy reasonable properties.