P2SLR: A Privacy-Preserving Sign Language Recognition as-a-Cloud Service
Using Deep Learning For Encrypted Gestures
Abstract
Cloud-based services have revolutionized data storage and processing
tasks. However, these services raise security concerns as service
providers may misuse the user’s stored data. Privacy loss is
particularly problematic for hearing and speech impaired individuals
that may need to use cloud infrastructure for sign language recognition
(SLR). Addressing these challenges, this paper presents a
privacy-preserving sign language recognition (P2SLR) as a cloud-service
that operates over cloud infrastructure without revealing the
individual’s visual information to the cloud service provider (CSP). The
proposed P2SLR system is realized through two innovations: (a)
block-based probabilistic image encryption scheme that combines
fractional-order chaotic system (FOCS) and singular value decomposition
(SVD) to obfuscate the visual information in video frames, and (b) a
cloud-residing deep convolutional neural network (DCNN) based
recognition architecture with a modified classifier to recognize
gestures from encrypted video. The proposed scheme is validated for
American, Argentinian, and German sign languages. The proposed scheme
achieved recognition accuracy in the range 90:76 - 98:09%, comparable
to existing state-of-the-art SLR techniques in the plain domain (PD).
The proposed image encryption scheme is secure under standard
cryptographic image attacks, protecting the individual’s identity. P2SLR
is the first move towards developing a secure SLR system over the cloud.