TY - GEN
T1 - Content-Based Secure Image Retrieval in an Untrusted Third-Party Environment
AU - Sengar, Sandeep Singh
AU - Kumar, Sumit
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023/4/26
Y1 - 2023/4/26
N2 - In this digital world, where availability of the image-generating tools is quite common and owing to the rapid growth of Internet knowledge, people use to exchange massive volume of images every day which results in creating large image repositories. So, retrieving appropriate image available on these repositories is one of the vital tasks. This problem leads to evolving content-based image retrieval (CBIR). As the generation of image increases, people start transferring these images to a remote third-party server, but these images may have personal information. This leads to adding privacy concerns toward the system as transferring personal data to some other place might be a cause of leakage of information or transfer to an unauthorized person. So, to keep this in mind, sensitive images like medical and personal images require encryption before being a contracted out for the privacy-preserving resolutions. In this work, we have deployed ACM for image encryption as well as asymmetric scalar product preserving encryption (ASPE) for feature vector encryption and similarity matching. We have demonstrated our results based on various benchmark databases.
AB - In this digital world, where availability of the image-generating tools is quite common and owing to the rapid growth of Internet knowledge, people use to exchange massive volume of images every day which results in creating large image repositories. So, retrieving appropriate image available on these repositories is one of the vital tasks. This problem leads to evolving content-based image retrieval (CBIR). As the generation of image increases, people start transferring these images to a remote third-party server, but these images may have personal information. This leads to adding privacy concerns toward the system as transferring personal data to some other place might be a cause of leakage of information or transfer to an unauthorized person. So, to keep this in mind, sensitive images like medical and personal images require encryption before being a contracted out for the privacy-preserving resolutions. In this work, we have deployed ACM for image encryption as well as asymmetric scalar product preserving encryption (ASPE) for feature vector encryption and similarity matching. We have demonstrated our results based on various benchmark databases.
UR - http://www.scopus.com/inward/record.url?scp=85161378684&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-7513-4_26
DO - 10.1007/978-981-19-7513-4_26
M3 - Conference contribution
AN - SCOPUS:85161378684
SN - 9789811975127
T3 - Smart Innovation, Systems and Technologies
SP - 287
EP - 297
BT - Evolution in Computational Intelligence - Proceedings of the 10th International Conference on Frontiers in Intelligent Computing
A2 - Bhateja, Vikrant
A2 - Yang, Xin-She
A2 - Lin, Jerry Chun-Wei
A2 - Das, Ranjita
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th International Conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2022
Y2 - 18 June 2022 through 19 June 2022
ER -