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Towards Secure Cloud Data Similarity Retrieval: Privacy Preserving Near-Duplicate Image Data Detection

  • Yulin Wu
  • Xuan Wang
  • Zoe L. Jiang
  • Xuan Li
  • Jin Li
  • S. M. Yiu
  • Zechao Liu
  • Hainan Zhao
  • Chunkai Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)

Abstract

As the development of cloud computing technology, cloud storage service has been widely used these years. People upload most of their data files to the cloud for saving local storage space and making data sharing available everywhere. Except for storage service, data similarity retrieval is another basic service that cloud provides, especially for image data. As demand for near-duplicate image detection increases, it has been an attracted research topic in cloud image data similarity retrieval in resent years. However, due to some image data (like medical images and face recognition images) contains important privacy information, it is preferred to support privacy protection in cloud image data similarity retrieval. In this paper, focusing on image data stored in the cloud, we propose a privacy preserving near-duplicate image data detection scheme based on the LSH algorithm. In particular, users would use their own image data to generate image-feature LSH metadata vector using LSH algorithm and would store both the ciphertexts of image data and image-feature LSH metadata vector in cloud. When the inquirer queries the near-duplicate image data, he would generate the image-feature query token LSH metadata vector using LSH algorithm and send it to cloud. With the query token, cloud will execute the privacy-preserving near-duplicate image data detection and return the encrypted result to inquirer. Then the inquirer would decrypt the ciphertext and get the final result. Our security and performance analysis shows that the proposed scheme achieves the goals of privacy preserving and lightweight.

Keywords

Near-duplicate Privacy preserving LSH algorithm Cloud image data Lightweight 

Notes

Acknowledgments

This work is supported by Basic Reasearch Project of Shenzhen of China (No. JCYJ20160318094015947, JCYJ20170307151518535), National Key Research and Development Program of China (No. 2017YFB0803002), The Natural Science Foundation of Fujian Province, China (No. 2017J05099), and National Natural Science Foundation of China (No. 61472091).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yulin Wu
    • 1
  • Xuan Wang
    • 1
  • Zoe L. Jiang
    • 1
  • Xuan Li
    • 2
  • Jin Li
    • 3
  • S. M. Yiu
    • 4
  • Zechao Liu
    • 1
  • Hainan Zhao
    • 1
  • Chunkai Zhang
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of Technology (Shenzhen)ShenzhenChina
  2. 2.College of mathematics and informaticsFujian Normal UniversityFuzhouChina
  3. 3.School of Computational Science and Education SoftwareGuangzhou UniversityGuangzhouChina
  4. 4.The University of Hong KongPok Fu LamHong Kong SAR, China

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