GaitPrivacyON: Privacy-preserving mobile gait biometrics using unsupervised learning.
Paula Delgado-SantosRubén TolosanaRichard M. GuestRubén Vera-RodríguezFarzin DeraviAythami MoralesPublished in: Pattern Recognit. Lett. (2022)
Keyphrases
- privacy preserving
- unsupervised learning
- human identification
- gait recognition
- gait analysis
- person identification
- privacy preserving data mining
- vertically partitioned data
- mobile phone
- privacy preservation
- mobile devices
- privacy sensitive
- record linkage
- supervised learning
- semi supervised
- pattern recognition
- sensitive information
- dimensionality reduction
- private information
- privacy issues
- multi party
- differential privacy
- horizontally partitioned data
- privacy concerns
- privacy protection
- feature selection
- mobile environments
- data privacy
- mobile applications
- sensitive data
- machine learning
- scalar product
- privacy preserving classification
- face recognition
- data perturbation
- feature extraction
- mobile users
- biometric systems
- preserving privacy
- data publishing
- medical images
- partitioned data
- horizontally partitioned
- naive bayesian classification
- privacy preserving association rule mining
- data sets