Enabling secure and effective near-duplicate detection over encrypted in-network storage

Cui, Helei; Yuan, Xingliang; Zheng, Yifeng

doi:10.1109/infocom.2016.7524346

Cited by 27 publications

(27 citation statements)

References 17 publications

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“…Near-duplicate detection (NDD) is a general data caching function that can help to reuse near-duplicate data and alleviate network traffic congestion. Cui et al [55], [57] proposed a secure NDD function by resorting to multikey searchable encryption (MKSE) [161] to enable queries on encrypted content uploaded from multiple users under different secret keys. Using MKSE, the service provider can transform the user's query encrypted with her own secret key into the form of different content providers' keys.…”

Section: Fig 5 Architecture Of Blindbox [24] In the Setup Phase The Endpoint Prepares A Garbled Aes Embedded With The Encryption Key On Tmentioning

confidence: 99%

Building In-the-Cloud Network Functions: Security and Privacy Challenges

et al. 2021

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Section: Fig 5 Architecture Of Blindbox [24] In the Setup Phase The Endpoint Prepares A Garbled Aes Embedded With The Encryption Key On Tmentioning

confidence: 99%

Building In-the-Cloud Network Functions: Security and Privacy Challenges

et al. 2021

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“…Hand gesture recognition [63] 9. Resource utilization and traffic alleviation in many network architectures and leveraging in-network storage for various content-centric sessions [117] 10. Visual media intelligence in detecting similar visual content published on the web in a short period of time [118] 9 Challenges and Future Research Directions A major challenge for the NDD is the runtime performance.…”

Section: Applications Of Near-duplicate Detection Of Imagesmentioning

confidence: 99%

A Review on Near-Duplicate Detection of Images using Computer Vision Techniques

Thyagharajan

Kalaiarasi

2020

Arch Computat Methods Eng

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Nowadays, digital content is widespread and simply redistributable, either lawfully or unlawfully. For example, after images are posted on the internet, other web users can modify them and then repost their versions, thereby generating near-duplicate images. The presence of near-duplicates affects the performance of the search engines critically. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from digital images. The main application of computer vision is image understanding. There are several tasks in image understanding such as feature extraction, object detection, object recognition, image cleaning, image transformation, etc. There is no proper survey in literature related to near duplicate detection of images. In this paper, we review the state-of-the-art computer vision-based approaches and feature extraction methods for the detection of near duplicate images. We also discuss the main challenges in this field and how other researchers addressed those challenges. This review provides research directions to the fellow researchers who are interested to work in this field.

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“…Near-duplicate data scanning for encrypted data has been studied. A lot of wastage in the storage system occurs due to these duplicates which is very costly to afford [9].…”

Section: Literature Reviewmentioning

confidence: 99%