Real-time privacy-preserving moving object detection in the cloud

Chu, Kuan-Yu; Kuo, Yin-Hsi; Hsu, Winston H.

doi:10.1145/2502081.2502157

Cited by 33 publications

(8 citation statements)

References 4 publications

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“…Hsu et al Hsu et al (2012) proposed a model for extracting SIFT features in ED using the Paillier cryptosystem. Chu et al Chu et al (2013) presented a real-time object detection model for video surveillance. The scheme trans-formed each frame into multiple blocks, and then each block is multiplied by a random matrix.…”

Section: Privacy-preserving Image Processingmentioning

confidence: 99%

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Tanwar¹,

Raman²,

Bhargava³

2020

Preprint

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<div>Object removal is a technique for removing the undesired object(s) and then fill-in the empty region(s) in an image such that the modified image is visually plausible. The existing algorithms are unable to provide promising results when the region to be removed - has varying textured-neighborhood, is small in size and the depth of the image and, is of specific geometric shapes such as triangle</div><div>and rectangle. In this paper, we proposed a new algorithm by incorporating the merits of partial differential equations (PDEs) and exemplar-based schemes to address these challenges. The data term, which measures the continuity of</div><div>isophotes in exemplar-based methods, is modified by incorporating a regularizer term and partial derivatives up to second order of the input image. This regularizer enhances the strength of isophotes striking the boundary and boosts</div><div>the information propagation in an unbiased manner, in terms of pixel intensity values. Additionally, the low-cost, agility, and accessing flexibility benefits of cloud services have attracted user’s attention today. Besides, users are concerned about utilizing them for their data, as they are supported by untrusted third parties. Addressing these privacy concerns for object-removal in an image over the cloud server, we extended and modified our algorithm to make it compatible for (T; N)-threshold Shamir secret sharing scheme (SSS). This privacy-preserving system is an end-to-end system for object-removal in the ED over the cloud server namely Crypt-OR. Crypt-OR is evaluated by removing synthetically imposed objects in real-images. Further, Crypt-OR has proved to be secure under various pixel-based cryptographic attacks such as frequency-known attack and pixel-correlation attack. </div>

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Section: Privacy-preserving Image Processingmentioning

confidence: 99%

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Tanwar¹,

Raman²,

Bhargava³

2020

Preprint

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“…In contrast, several works proposed to protect the image/video data using block-based transformation algorithms, which provide better efficiency than the homomorphic algorithms. This type of method [1,8,10,17,26] typically decompose the input image/video into equal-sized blocks, and then each block of data is transformed or encrypted separately. As the format of the volume data to some extent resembles the format of the image/video data, the proposed volume data transformation algorithm then adopts a similar mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…It is because of three main reasons: (1) the voxel values in different sub-volumes are transformed separately. (2) The sub-volume permutation makes the determination of neighboring sub-volumes more complex.…”

Section: The Security Level During the Remote Renderingmentioning

confidence: 99%

Obfuscated volume rendering

Chou

Yang

2015

Vis Comput

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Analyzing and processing various data types in a privacy-preserving perspective has been researched in many disciplines; however, such an issue draws very limited attention in the research field of scientific visualization. We wondered if it is possible to delegate the rendering of a volume data set to a remote server(s) while still being able to preserve its privacy to certain extent. This paper presents a block-based volume data transformation algorithm that obfuscates a volume data set so as to reduce the user's privacy concern when the volume data set is to be uploaded to a remote server. In addition, a privacy-aware transfer function adjustment is proposed so that not only the privacy is protected during the rendering process, but also the computational loading could be leveraged to the server side as much as possible. Experimental results show that the proposed method yields visually satisfactory results compared with a normal direct volume rendering approach. Moreover, the decrease of the rendering efficiency caused by the proposed method is still controlled within an acceptable range. A case study proves that the proposed approach can be adopted in practice. This work explores the possibility of rendering a volume data set through remote server(s) while the privacy of data is still maintained. Electronic supplementary materialThe online version of this article (

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“…However, traditional video encryption methods which "randomize" all the information cannot support abnormal event detection on the encrypted video. Thus authors of [4], [5] proposed to encrypt videos in a particular way that can preserve some statistical information to facilitate abnormal event detection on the encrypted videos. Whole video encryption typically encrypts the videos in the pixel domain to facilitate abnormal event detection, thus significantly reducing the efficiency of subsequent compression.…”

Section: Introductionmentioning

confidence: 99%

Lossless ROI Privacy Protection of H.264/AVC Compressed Surveillance Videos

Zeng

Yang

et al. 2016

IEEE Trans. Emerg. Topics Comput.

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Privacy becomes one of the major concerns of video surveillance systems especially in cloud-based systems. Privacy protection of surveillance videos aims to protect privacy information without hampering normal video surveillance tasks. ROI (Region-Of-Interest) privacy protection is more practical compared to the whole video encryption approaches. However, one common drawback of virtually all current ROI privacy protection methods is that the original compressed surveillance video recorded in the camera is permanently distorted by the privacy protection process, due to the quantization in the re-encoding process. Thus the integrity of the original compressed surveillance video captured by the camera is destroyed. This is unacceptable for some application scenarios such as video forensics for investigations and video authentication for law enforcement, et al. In this paper, we introduce a new paradigm for privacy protection in surveillance videos, referred to as lossless privacy region protection, which has the property that the distortion introduced by the protection of the privacy data can be completely removed from the protected videos by authorized users. We demonstrate the concept of lossless privacy region protection through a proposed scheme applied on H.264/AVC compressed videos.

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Real-time privacy-preserving moving object detection in the cloud

Cited by 33 publications

References 4 publications

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Obfuscated volume rendering

Lossless ROI Privacy Protection of H.264/AVC Compressed Surveillance Videos

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