# A Privacy-Preserving Image Retrieval Scheme Using A Codebook Generated From Independent Plain-Image Dataset

arXiv: https://arxiv.org/abs/2109.01841 · HTML: https://mlsanity.com/paper/2109.01841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 54 references that are in the map.
- First posted on arXiv 4 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Encryption-then-Compression Systems using Grayscale-based Image Encryption for JPEG Images](https://mlsanity.com/paper/1811.00236/) (2018)
- [Attacks on Image Encryption Schemes for Privacy-Preserving Deep Neural Networks](https://mlsanity.com/paper/2004.13263/) (2020)
- [A Privacy-Preserving Content-Based Image Retrieval Scheme Allowing Mixed Use Of Encrypted And Plain Images](https://mlsanity.com/paper/2011.00270/) (2020)

## Abstract (arXiv)

In this paper, we propose a privacy-preserving image-retrieval scheme using a codebook generated by using a plain-image dataset. Encryption-then-compression (EtC) images, which were proposed for EtC systems, have been used in conventional privacy-preserving image-retrieval schemes, in which a codebook is generated from EtC images uploaded by image owners, and extended SIMPLE descriptors are then calculated as image descriptors by using the codebook. In contrast, in the proposed scheme, a codebook is generated from a dataset independent of uploaded images. The use of an independent dataset enables us not only to use a codebook that does not require recalculation but also to constantly provide a high retrieval accuracy. In an experiment, the proposed scheme is demonstrated to maintain a high retrieval performance, even if codebooks are generated from a plain image dataset independent of image owners' encrypted images.