LocID: A Secure and Usable Location-Based Smartphone Unlocking Scheme Using Wi-Fi Signals and Light Intensity

A user's location information can be used to identify the user. For example, in Android, we can keep our smartphone unlocked when it is located near a place that was previously registered as a trusted place. However, existing location-based user authentication solutions failed to support fine-grained indoor location registration. In this article, we present a highly accurate identification scheme named location-based identification (LocID) using the smartphone's physical fingerprints that are sensed at the user's secure area. Our key idea is to use Wi-Fi signals and the light intensity of smartphones' fingerprints to accurately identify their locations within adjacent indoor areas. To generate stable and reliable location fingerprints, we develop a novel mathematical location-wise signal signature (LSS) algorithm by minimizing the bias highly affected by various environmental factors. To show the feasibility of LocID, we evaluated the performance using machine learning classifiers with a real-world data set containing Wi-Fi and light fingerprints from 68 locations in a building for five weeks. The experimental results show that LocID achieved an F1-score of 98.3%, false-negative rate (FNR) of 2.9%, and false-positive rate (FPR) of 1.7%, respectively, when LocID was retrained daily. Moreover, LocID achieved an F1-score of 94.4%, FNR of 2.7%, and FPR of 3.7%, respectively, when LocID was trained using the data set during the first week and tested with the remaining four weeks data set. Also, we tested LocID using only one-scan of location fingerprints and achieved 90.6% and 90.8% AUC scores for support vector machine and K-nearest neighbor classifiers, respectively.