Similarity Search for Encrypted Images in Secure Cloud Computing ||JAVA PROJECT || 2021

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Name:Venkatarao Ganipisetty
Mobile:+91 9966499110
Email :[email protected]
ABOUT PROJECT:
With the emergence of intelligent terminals, the Content-Based Image Retrieval (CBIR) technique has attracted muchattention from many areas (i.e., cloud computing, social networking services, etc.). Although existing privacy-preserving CBIR schemescan guarantee image privacy while supporting image retrieval, these schemes still have inherent defects (i.e., low search accuracy, lowsearch efficiency, key leakage, etc.). To address these challenging issues, in this paper we provide a similarity Search for EncryptedImages in secure cloud computing (called SEI). First, the feature descriptors extracted by the Convolutional Neural Network (CNN)model are used to improve search accuracy. Next, an encrypted hierarchical index tree by using K-means clustering based on AffinityPropagation (AP) clustering is devised, which can improve search efficiency. Then, a limited key-leakage k-Nearest Neighbor (kNN)algorithm is proposed to protect key from being completely leaked to untrusted image users. Finally, SEI is extended to further preventimage users’ search information from being exposed to the cloud server. Our formal security analysis proves that SEI can protectimage privacy as well as key privacy. Our empirical experiments using a real-world dataset illustrate the higher search accuracy andefficiency of SEI.


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