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Author: search.filters.author.Rajput, Amitesh SinghSubject: search.filters.subject.Cloud computing

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    Example Based Privacy-Preserving Video Color Grading
    (Springer, 2019) Rajput, Amitesh Singh
    The integration of cloud computing and smart multimedia gadgets has made an attractive business model today. However, data privacy is one of the major concern when moving to third party driven infrastructures like cloud. Furthermore, due to diverse camera sensors, the captured multimedia may contain insufficient lightning/colors and processing them manually is a painstakingly task. A few schemes have been proposed to address this problem, however they suffered from the major drawback of computational and storage overhead, and becomes non-trivial in case of videos. Considering these challenges, we propose an automatic video color grading approach in this chapter. The proposed approach enables cloud data center to process encrypted multimedia data by transferring its colors as per an example image as the reference. We analyze the correlation between consecutive video sequences and propose to evaluate the color transformation parameters for every alternate video frame. In addition, proxy encryption based Paillier cryptosystem has been used for video encryption. As a result, the computational and storage overheads are drastically reduced with effective video grading results. The feasibility and robustness of the proposed approach are validated through various tests.
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    CryptFine: Towards secure cloud based filtering in encrypted domain
    (IEEE, 2017) Rajput, Amitesh Singh
    Cloud computing is a shift towards increasing the capabilities dynamically without investing in new infrastructure. Enormous storage of data and high-speed computing to customers over the internet can be accomplished using cloud. Security of data in cloud is one of the chief concerns which acts as an obstacle in the realization of cloud computing. Along with quick expansion and application of cloud computing, users concern more and more regarding security and privacy issues involved in these techniques. An efficient method to perform privacy preserving image filtering operations in encrypted domain over cloud is proposed in this paper. The proposed method provides facility for distributed secure processing and storage of the secret image using Shamir's secret sharing method and Chinese remainder theorem. The plain secret image is divided into number of shares in a manner that each share by itself does not reveal any meaningful information about the original image, while collectively, they retain all the information. Two secret keys are used to avail security of image shares and without knowledge of such keys, one cannot restore the secret image back. Hence, the proposed scheme is highly secured and the image can be processed without revealing any information at the cloud data center. Experimental results demonstrate that number of image filtering procedures for enhancement can be carried out efficiently in encrypted domain using the proposed method.
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    Color me, store me, know me not: Supporting image color transfer and storage in encrypted domain over cloud
    (IEEE, 2017) Rajput, Amitesh Singh
    Color transfer is a well known methodology to transfer color between images such that color palette of the resulting image should match as of the reference image. Current trend towards cloud computing has initiated the requirement of performing color transfer remotely by untrusted third party servers. To address this requirement, we present a system that addresses the challenge of performing color transfer between images when the test images are in encrypted form. While standard methods accomplish the task of color transfer in plain domain, our approach is the first known scheme to perform color transfer in encrypted domain. Furthermore, the proposed approach also solves the problem of privacy preserving storage over cloud infrastructures given that the test images can remain in encrypted form without requiring any decryption. Experimental results and security analysis demonstrate the potential and effectiveness of our approach with various application scenarios.
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    Privacy-preserving smart surveillance using local color correction and optimized ElGamal cryptosystem over cloud
    (IEEE, 2019) Rajput, Amitesh Singh
    The emergence of cloud computing in integration with smart multimedia devices has created an attractive business model today. However, due to the involvement of third party servers, there is a risk of privacy for highly confidential data like surveillance images/videos. Moreover, due to inconsistent lightning conditions, there is a usual requirement of post-processing the captured multimedia for better appearance. Addressing these problems, we propose a novel cloud based privacy-preserving approach for image color enhancement in this paper. Unlike existing color correction schemes, where colors of the test image are processed in plain domain with visible image contents, we propose to perform color correction operations in the encrypted domain over cloud. As a consequence, superior results are achieved along with complete privacy assurance. In addition, we propose a block-based image encryption method using logistic-tent system and ElGamal cryptosystem. As a result, size of the encrypted image is significantly reduced as compared to the naive approach. Experimental results are performed under various tests and the proposed approach is found to be highly effective as compared to state-of-the-art schemes. Moving ahead, security strength of the proposed approach is demonstrated through a challenge response game model.
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    SecureDL: A privacy preserving deep learning model for image recognition over cloud
    (Elsevier, 2022-07) Rajput, Amitesh Singh
    The key benefits of cloud services such as low cost, access flexibility, and mobility have attracted worldwide users to utilize deep learning algorithms for computer vision. These cloud servers are maintained by third parties, where users are always concerned about sharing their confidential data with them. In this paper, we addressed these concerns for by developing SecureDL, a privacy-preserving image recognition model for encrypted data over cloud. The proposed block-based image encryption scheme is well designed to protect image’s visual information. The scheme constitutes an order-preserving permutation ordered binary number system and pseudo-random matrices. The proposed method is proved to be secure in a probabilistic viewpoint, and using various cryptographic attacks. Experiments are conducted over several image recognition datasets, and the trade-off analytics between the achieved recognition accuracy and data encryption is well described. SecureDL overcomes the storage and computational overheads that occur with fully-homomorphic and multi-party computation based secure recognition schemes.