SecureDL: A privacy preserving deep learning model for image recognition
over cloud
- Vishesh Kumar Tanwar ,
- Balasubramanian Raman ,
- Amitesh Singh Rajput ,
- Rama Bhargava
Abstract
The key benefits of cloud services, such as low cost, access
flexibility, and mobility, have attracted users worldwide to utilize the
deep learning algorithms for developing computer vision tasks. Untrusted
third parties maintain these cloud servers, and users are always
concerned about sharing their confidential data with them. In this
paper, we addressed these concerns for by developing SecureDL, a
privacy-preserving image recognition model for encrypted data over
cloud. Additionally, we proposed a block-based image encryption scheme
to protect images' visual information. The scheme constitutes an
order-preserving permutation ordered binary number system and
pseudo-random matrices. The encryption scheme is proved to be secure in
a probabilistic viewpoint and through various cryptographic attacks.
Experiments are performed for several image recognition datasets, and
the achieved recognition accuracy for encrypted data is close with
non-encrypted data. SecureDL overcomes the storage, and computational
overheads occurred in fully-homomorphic and multi-party computations
based secure recognition schemes.