English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

To ensure the privacy of sensitive data used in the training of deep learningmodels, a number of privacy-preserving methods have been designed by theresearch community. However, existing schemes are generally designed to workwith textual data, or are not efficient when a large number of images is usedfor training. Hence, in this paper we propose a lightweight and efficientapproach to preserve image privacy while maintaining the availability of thetraining set. Specifically, we design the pixel block mixing algorithm forimage classification privacy preservation in deep learning. To evaluate itsutility, we use the mixed training set to train the ResNet50, VGG16,InceptionV3 and DenseNet121 models on the WIKI dataset and the CNBC facedataset. Experimental findings on the testing set show that our schemepreserves image privacy while maintaining the availability of the training setin the deep learning models. Additionally, the experimental results demonstratethat we achieve good performance for the VGG16 model on the WIKI dataset andboth ResNet50 and DenseNet121 on the CNBC dataset. The pixel block algorithmachieves fairly high efficiency in the mixing of the images, and it iscomputationally challenging for the attackers to restore the mixed training setto the original training set. Moreover, data augmentation can be applied to themixed training set to improve the training's effectiveness.

A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep Learning