Secure Content-Based Image Retrieval in the Cloud with Key Confidentiality

Jung Shian Li, I. Hsien Liu, Chin Jui Tsai, Zhi Yuan Su, Chu Fen Li, Chuan Gang Liu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Owing to the rapid development of cloud services and personal privacy demand, secure cloud storage services and search over encrypted datasets have become an important issue. Recently, the leaking of images such as identification and driver's licenses catches much attention. The trend towards secure computation has been widely discussed, especially asymmetric scalar-product-preserving encryption (ASPE) and homomorphic encryption (HE). Although ASPE have ability to encrypt and determine the similarity between ciphertexts efficiently, it is not a practical methodology due to its assumption that the users are fully trusted in real world and it also may have key leakage problem. Contrary to ASPE, HE can execute addition and multiplication in the encrypted domain and solve key leakage problem. Hence, in this paper, we combine the opinions of HE and ASPE to propose new privacy-preserving content-based image retrieval with key confidentiality scheme against the attacks from data owner, cloud server and users. Our privacy preserving image retrieval scheme is developed under strong threat model that is close to real world. Furthermore, to the best of our knowledge, our work is the first one that developing scheme under the assumption that all the entities involved in privacy-preserving image retrieval system are semi-trusted. Our scheme ensures the confidentiality of key and privacy of query information at the same time. In addition, we provide a lightweight verification to check whether the search query is fake or not. Finally, the experiment results show that the computation overheads and search precision are acceptable at the same time.

Original languageEnglish
Article number9121991
Pages (from-to)114940-114952
Number of pages13
JournalIEEE Access
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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