Content-based image retrieval in homomorphic encryption domain

Bellafqira, Reda; Coatrieux, Gouenou; Bouslimi, Dalel

doi:10.1109/embc.2015.7319009

Cited by 31 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach also extracts the features from the encrypted regions, since the feature extraction algorithm can't differentiate the encrypted and unencrypted regions. Homomorphic encryption is also used to preserve privacy, where the authors Bellafqira et al [14] used a histogram of wavelet coefficients that was extracted from the encrypted image that was extracted by the Paillier algorithm. The similarity between the features present in the database and the features of the query image are matched using 𝐿 1 distance.…”

Section: Related Workmentioning

confidence: 99%

High and Low-Level Classification based Features for Content-based Image Retrieval in Privacy-Preserving

selvapattu

Nair

2022

Preprint

View full text Add to dashboard Cite

Due to the ease of access and high storage, cloud storage has become very popular in recent days. However, uploading the image directly to the cloud server can cause the risk of privacy leakage. This paper proposes a content-based image retrieval (CBIR) mechanism by preserving the privacy content of the image. The approach initially classifies the group of image pixels into two categories namely high-level class and low-level class. The low-level class pixels are encrypted by a local encryption algorithm that encrypts. The paper proposes two encryption mechanisms namely encryption without high-level block scrambling (WO-HLBS) and encryption with high-level block scrambling (W-HLBS) that uses the position-dependent local binary pattern feature (PD-LBP) and position-independent local binary pattern feature (PI-LBP) feature extraction. The number of high-level features is chosen by the feature selection factor. The image that was encrypted was uploaded to a cloud server where the cloud server detects and extracts the features from the high-level features and store them in the cloud database. During the image retrieval, the high-level features that are extracted from the encrypted query image are matched with the features present in the cloud database to obtain the retrieval results. The evaluation of the proposed image retrieval was evaluated using the Inria Holidays dataset using the metrics such as retrieval precision and time complexity. The scheme WO-HLBS and W-HLBS provide a mean average precision of about 0.679 and 0.6295 respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

High and Low-Level Classification based Features for Content-based Image Retrieval in Privacy-Preserving

selvapattu

Nair

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Supposing there are N images with M -dimension features in the database, the proposed algorithm implements O(MN ) times of multiplication and exponentiation operations. Unlike the work [242] which exploits local image features, the method in [243] was the first secure CBIR scheme over global image features, based on the wavelet transform [258]. The operations of image retrieval are performed by the cloud server on the encrypted domain, due to the additively homomorphic property of the Paillier cryptosystem.…”

Section: ) Image Feature Extractionmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

View full text Add to dashboard Cite

With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

show abstract

“…The query results are obtained through homomorphic operations between the encrypted query and the encrypted data. A lot of researchers have proposed secure information retrieval schemes based on homomorphic encryption [33][34][35][36]. Meng Shen et al proposed a graph encryption scheme which makes use of SWHE and enables approximate Constrained Shortest Distance (CSD) querying over encrypted graph [37].…”

Section: Applications Of Homomorphic Encryption Schemesmentioning

confidence: 99%

A More Efficient Fully Homomorphic Encryption Scheme Based on GSW and DM Schemes

Wang

Luo

2018

Security and Communication Networks

View full text Add to dashboard Cite

Achieving both simplicity and efficiency in fully homomorphic encryption (FHE) schemes is important for practical applications. In the simple FHE scheme proposed by Ducas and Micciancio (DM), ciphertexts are refreshed after each homomorphic operation. And ciphertext refreshing has become a major bottleneck for the overall efficiency of the scheme. In this paper, we propose a more efficient FHE scheme with fewer ciphertext refreshings. Based on the DM scheme and another simple FHE scheme proposed by Gentry, Sahai, and Waters (GSW), ciphertext matrix operations and ciphertext vector additions are both applied in our scheme. Compared with the DM scheme, one more homomorphic NOT AND (NAND) operation can be performed on ciphertexts before ciphertext refreshing. Results show that, under the same security parameters, the computational cost of our scheme is obviously lower than that of GSW and DM schemes for a depth-2 binary circuit with NAND gates. And the error rate of our scheme is kept at a sufficiently low level.

show abstract

Content-based image retrieval in homomorphic encryption domain

Cited by 31 publications

References 9 publications

High and Low-Level Classification based Features for Content-based Image Retrieval in Privacy-Preserving

High and Low-Level Classification based Features for Content-based Image Retrieval in Privacy-Preserving

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

A More Efficient Fully Homomorphic Encryption Scheme Based on GSW and DM Schemes

Contact Info

Product

Resources

About