# Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support

arXiv: https://arxiv.org/abs/2103.06453 · HTML: https://mlsanity.com/paper/2103.06453/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [EIE: Efficient Inference Engine on Compressed Deep Neural Network](https://mlsanity.com/paper/1602.01528/) (2016)
- [Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks](https://mlsanity.com/paper/1705.01626/) (2017)
- [C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs](https://mlsanity.com/paper/1803.06305/) (2018)
- [Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning](https://mlsanity.com/paper/1708.09754/) (2017)
- [Continuous Authentication Using One-class Classifiers and their Fusion](https://mlsanity.com/paper/1710.11075/) (2017)
- [Multi-sensor authentication to improve smartphone security](https://mlsanity.com/paper/1703.03378/) (2017)

## Built on by (most cited first, 1 of 1)

- [CloudShield: Real-time Anomaly Detection in the Cloud](https://mlsanity.com/paper/2108.08977/) (2021)

## Abstract (arXiv)

Impostors are attackers who take over a smartphone and gain access to the legitimate user's confidential and private information. This paper proposes a defense-in-depth mechanism to detect impostors quickly with simple Deep Learning algorithms, which can achieve better detection accuracy than the best prior work which used Machine Learning algorithms requiring computation of multiple features. Different from previous work, we then consider protecting the privacy of a user's behavioral (sensor) data by not exposing it outside the smartphone. For this scenario, we propose a Recurrent Neural Network (RNN) based Deep Learning algorithm that uses only the legitimate user's sensor data to learn his/her normal behavior. We propose to use Prediction Error Distribution (PED) to enhance the detection accuracy. We also show how a minimalist hardware module, dubbed SID for Smartphone Impostor Detector, can be designed and integrated into smartphones for self-contained impostor detection. Experimental results show that SID can support real-time impostor detection, at a very low hardware cost and energy consumption, compared to other RNN accelerators.