Privacy-Preserving Content-Based Image Retrieval in the Cloud

Ferreira, Bernardo; Rodrigues, João; Leitão, João; Domingos, Henrique

doi:10.1109/srds.2015.27

Cited by 62 publications

(25 citation statements)

References 27 publications

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“…In the second category, Ferreira et al [19] proposed a scheme IES-CBIR that can extract image features from encrypted images. The texture and color features were encrypted separately; the color feature was encrypted by scrambling pixels in HSV color model and the texture feature is encrypted by shuffling rows and columns in images.…”

Section: Related Workmentioning

confidence: 99%

Content-based multi-source encrypted image retrieval in clouds with privacy preservation

Shen

Cheng

Zhu

et al. 2020

Future Generation Computer Systems

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Content-based image retrieval (CBIR) is one of the fundamental image retrieval primitives. Its applications can be found in various areas, such as art collections and medical diagnoses. With an increasing prevalence of cloud computing paradigm, image owners desire to outsource their images to cloud servers. In order to deal with the risk of privacy leakage of images, images are typically encrypted before they are outsourced to the cloud, which makes CBIR an extremely challenging task. Existing studies focus on the scenario with only a single image owner, leaving the problem of CBIR with multiple image sources (i.e., owners) unaddressed.In this paper, we propose a secure CBIR scheme that supports Multiple Image owners with Privacy Protection (MIPP). We encrypt image features with a secure multi-party computation technique, which allows image owners to encrypt image features with their own keys. This enables efficient image retrieval over images gathered from multiple sources, while guaranteeing that image privacy of an individual image owner will not be leaked to other image owners. We also propose a new method for similarity measurement of images that can avoid revealing image similarity information to the cloud. Theoretical analysis and experimental results demonstrate that MIPP achieves retrieval accuracy and efficiency simultaneously, while preserving image privacy.

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Section: Related Workmentioning

confidence: 99%

Content-based multi-source encrypted image retrieval in clouds with privacy preservation

Shen

Cheng

Zhu

et al. 2020

Future Generation Computer Systems

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“…This solution enables both encrypted storage and searching using CBIR queries while preserving privacy. Encrypted data storage and searching using CBIR queries while preserving privacy but there have key sharing, user access control, and reliability issues [6]. In this work, they propose a privacypreserving content-based image retrieval scheme, which allows the data owner to outsource the image database and CBIR service to the cloud, without revealing the original content of the database to the cloud server.…”

Section: Related Workmentioning

confidence: 99%

Survey on Secure and Efficient Content-Based Retrieval in Cloud Image Repositories

S¹

2020

IJRASET

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Many cloud stages ascend to meet sincere prerequisites for broad volume of individual picture store, sharing and search. Truth be told the vast majority of those pictures contain delicate secure information (e.g., Human, territory and any event) and people's insurance concerns ruin their venture into untrusted administrations, the present cloud stages give little help to picture security confirmation that is security assurance. Confronting extensive scale images from numerous clients i.e. user's, it is extremely challenging for the cloud to keep up the record structure and schedule parallel calculation without learning up anything about the image contents and indices. In this work, present a novel that is Privacy-protecting Content based Image Search framework on Cloud, or, in other words towards possible cloud services which give secure content based extensive scale image search with fine-grained access control using Shape-Based Invariant Texture Index (SITI). Users can search on others' images on the off chance that they are approved by the image owners. Larger part of the computationally serious part is handled by the cloud, and a querier can now simply send the query and get the search outcome. Specially, to manage enormous images, will designed our framework suitable for distributed and parallel calculation and introduce several optimizations with further facilitate the search procedure. Our security examination and model system assessment results will exhibit that this work adequately verifies the picture assurance expecting practically no exertion of computation and correspondence.

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“…Such a scenario uses the Private Set Union Cardinality (PSI-CA) and a third-party server. Ferreira et al [17] proposed a system to search encrypted images databases stored on a server maintained by a third-party service provider. Under both [16,17], the server should not know its stored data.…”

Section: Related Workmentioning

confidence: 99%

“…However, such a method assumes that the database is public when such database is supposed to be private. The proposed methods in [16][17][18] are also not suitable for evaluating similarity. These approaches can achieve an exact match, thereby limiting the ability to develop efficient solutions.…”

Section: Related Workmentioning

confidence: 99%

SEPIM: Secure and Efficient Private Image Matching

et al. 2016

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Matching a particular image within extensive datasets has become increasingly pressing in many practical fields. Hence, a number of matching methods have been developed when confidential images are used in image matching between a pair of security agencies, but they are limited by either search cost or search precision. In this paper, we propose a privacy-preserving private image matching scheme between two parties where images are confidential, namely secure and efficient private image matching (SEPIM). The descriptor set of the queried party needs to be generated and encrypted properly with the use of a secret key at the queried party side before being transferred to the other party. We present the development and validation of a secure scheme to measure the cosine similarity between two descriptor sets. To hasten the search process, we construct a tree-based index structure by utilizing the k-means clustering algorithm. The method can work without using any image encryption, sharing, and trusted third party. SEPIM is relatively efficient when set against other methods of searching images over plaintexts, and shows a higher search cost of just 14% and reduction in search precision of just 2%. We conducted several empirical analyses on real image collections to demonstrate the performance of our work.

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Privacy-Preserving Content-Based Image Retrieval in the Cloud

Cited by 62 publications

References 27 publications

Content-based multi-source encrypted image retrieval in clouds with privacy preservation

Content-based multi-source encrypted image retrieval in clouds with privacy preservation

Survey on Secure and Efficient Content-Based Retrieval in Cloud Image Repositories

SEPIM: Secure and Efficient Private Image Matching

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