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Image Feature Extraction in Encrypted Domain With Privacy-Preserving SIFT

机译:保留隐私的SIFT在加密域中提取图像特征

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Privacy has received considerable attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario where the server is resource-abundant, and is capable of finishing the designated tasks. It is envisioned that secure media applications with privacy preservation will be treated seriously. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first to target the importance of privacy-preserving SIFT (PPSIFT) and to address the problem of secure SIFT feature extraction and representation in the encrypted domain. As all of the operations in SIFT must be moved to the encrypted domain, we propose a privacy-preserving realization of the SIFT method based on homomorphic encryption. We show through the security analysis based on the discrete logarithm problem and RSA that PPSIFT is secure against ciphertext only attack and known plaintext attack. Experimental results obtained from different case studies demonstrate that the proposed homomorphic encryption-based privacy-preserving SIFT performs comparably to the original SIFT and that our method is useful in SIFT-based privacy-preserving applications.
机译:隐私已受到相当多的关注,但在多媒体社区中仍然被很大程度上忽略。考虑一个云计算场景,其中服务器资源丰富,并且能够完成指定的任务。可以预见,具有隐私保护功能的安全媒体应用将得到认真对待。鉴于尺度不变特征变换(SIFT)已在各个领域得到广泛采用,本文是第一个针对隐私保护SIFT(PPSIFT)的重要性并解决安全SIFT特征提取和处理问题的论文。加密域中的表示形式。由于必须将SIFT中的所有操作移至加密域,因此我们提出了基于同态加密的SIFT方法的隐私保护实现。通过基于离散对数问题和RSA的安全性分析,我们表明PPSIFT对于仅密文攻击和已知的明文攻击是安全的。从不同案例研究获得的实验结果表明,所提出的基于同态加密的隐私保护SIFT与原始SIFT的性能相当,并且我们的方法可用于基于SIFT的隐私保护应用程序。

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