User Identification on Smartphones with Motion Sensors and Touching Behaviors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The usage areas of smartphones are increasing day by day and users store different private and sensitive data on these devices. Therefore, the security of these devices, user identification and authentication on them are of critical importance. Traditional methods such as PIN, password and fingerprint used in user authentication can be exposed to various attacks and create security vulnerabilities. To address these issues, motion sensors and user biometrics are widely used to improve security mechanism in smart devices. In this paper, we show the feasibility of user identification using accelerometer sensor data on smartphones. For this purpose, we use accelerometer sensor data from 120 users, extract features to analyze differences in users’ smartphone interaction and identify users. For user identification, novelty detection and two-class classification algorithms are applied and their results are compared. As a result, it is show that motion sensors and touch behaviors can be used in user identification, 0.97 AUC (area under the curve) and 3.0% ERR (equal error rate) are obtained with proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it