SmartCAMPP - Smartphone-based continuous authentication leveraging motion sensors with privacy preservation
Pattern Recognition Letters, ISSN: 0167-8655, Vol: 147, Page: 189-196
2021
- 20Citations
- 28Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Continuous Authentication (CA) approaches are attracting attention due to the explosion of available sensors from IoT devices such as smartphones. However, a critical privacy concern arises when CA data is outsourced. Data from motion sensors may reveal users’ private issues. Despite the need for CA in smartphones, no previous work has explored how to tackle this matter leveraging motion sensors in a privacy-preserving way. In this work, a mechanism dubbed SmartCAMPP is proposed to achieve CA based on gyroscope and accelerometer data. Format-preserving encryption techniques are applied to privately outsource them. Our results show the suitability of the proposed scheme, featuring 76.85% of accuracy while taking 5.12 ms. of computation for authenticating each user. Interestingly, the use of cryptography does not lead to a significant impact as compared to a non-privacy-preserving mechanism.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0167865521001434; http://dx.doi.org/10.1016/j.patrec.2021.04.013; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85106311746&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0167865521001434; https://dx.doi.org/10.1016/j.patrec.2021.04.013
Elsevier BV
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