ShakeMe: Key Generation from Shared Motion

Abstract

Abstract-Devices equipped with accelerometer sensors such as today's mobile devices can make use of motion to exchange information. A typical example for shared motion is shaking of two devices which are held together in one hand. Deriving a shared secret (key) from shared motion, e.g. for device pairing, is an obvious application for this. Only the keys need to be exchanged between the peers and neither the motion data nor the features extracted from it. This makes the pairing fast and easy. For this, each device generates an information signal (key) independently of each other and, in order to pair, they should be identical. The key is essentially derived by quantizing certain well discriminative features extracted from the accelerometer data after an implicit synchronization. In this paper, we aim at finding a small set of effective features which enable a significantly simpler quantization procedure than the prior art. Our tentative results with authentic accelerometer data show that this is possible with a competent accuracy (76%) and key strength (entropy approximately 15 bits).

A Survey of Wearable Devices Pairing Based on Biometric Signals

“…Hash functions and heuristic search trees were leveraged in [104] to propose a key exchange protocol based on accelerometer data while the user shakes devices together. In another work, Yuzuguzel et al [105] propose to derive a shared key based on finding a small set of effective features from shaking of two devices that are held together in one hand. Gorza et al [106] investigated the pairing of mobile devices based on shared accelerometer data under various transportation environments.…”

“…Depending on the sensors used for pairing, different events can be considered as anchor points. For instance, the event can be a heel-strike [116], bumping the devices or shaking them together in one hand [105]. Although better synchronization reduces the bit mismatches at the next stage, the pairing protocol should support devices without a screen and keyboard and not impose strict synchronization on pairing devices when collecting ambient context information [138].…”

“…Therefore, in some cases, the Gray code can perform better in detecting noise in two consecutive samples [131]. In the recent pairing methods, various type of quantization approaches has been exploited, including pairwise nearest neighbor (PNN) quantization [101], standard decimal-to-binary quantizer [99], [105], decimal-to-Gray-code quantizer [123], [131], uniform quantizer [113], sigma-delta quantizer [106], and exploiting multiple thresholds [100], [102]- [104], [107]- [109], [114], [115], [117].…”

Secure Accelerometer-Based Pairing of Mobile Devices in Multi-Modal Transport

“…The server checks if the datasets match and then the devices can connect with each other, which has the significant disadvantage of requiring online connectivity. In [42] machine-learning is used for generating secret shared keys between devices. A distinct methodology with heuristic trees and hash functions is proposed in [12], addressing the vulnerability of low-entropy vectors in man-in-the-middle attacks in a previous protocol proposal [23].…”

Encryption by Heart (EbH)—Using ECG for time-invariant symmetric key generation

“…Other proposals generate keys from assorted biometric traits such as creating hashes from the palm of the hand [40], using handwritten signatures [41], moving a pair of devices simultaneously trying to get the same key [42], analyzing voice signals [43] or even studying a method to generate a key for digital audio watermarking [44].…”