Multimedia data needs huge storage space, and application of multimedia data needs powerful capability of computing. Cloud computing can help owner of multimedia data to deal with it. But, multimedia data on cloud may reveal privacy of data owner, such as sex, hobbies, address, looks, and so on. Data owner can encrypt multimedia data for confidentiality before uploading it to cloud. However, encrypted multimedia data makes its utilization difficult. In this paper, we first discover pre-existing schemes have problems of huge storage space, security and low efficiency due to their inefficient and insecure algorithms. Then, we provide an effective and practical privacy-preserving scale-invariant feature transform (SIFT) scheme for encrypted image. It uses leveled homomorphic encryption based on our new encoding schemes, our new homomorphic comparison, division and derivative encryption. Our new secure SIFT scheme can realize higher computing efficiency, greatly reduce communication costs and interactive times between user and server, and perform correct feature key point detection, accurate feature point description and image matching. We evaluate security and efficiency of our new secure SIFT scheme, and compare our new secure SIFT scheme with other schemes in detail. The result shows that it is closest to the original SIFT algorithm.

中文翻译：

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安全外包SIFT：加密域中高效且隐私保护的图像特征提取

多媒体数据需要巨大的存储空间，多媒体数据的应用需要强大的计算能力。云计算可以帮助多媒体数据的所有者处理它。但是，云端的多媒体数据可能会泄露数据所有者的隐私，例如性别、爱好、地址、长相等。数据所有者可以在将多媒体数据上传到云端之前对其进行加密以确保机密性。然而，加密的多媒体数据使其难以使用。在本文中，我们首先发现现有方案由于其算法效率低下和不安全，存在存储空间大、安全性和效率低的问题。然后，我们为加密图像提供了一种有效且实用的隐私保护尺度不变特征变换（SIFT）方案。它使用基于我们新编码方案的分级同态加密，我们新的同态比较，除法和衍生加密。我们新的安全 SIFT 方案可以实现更高的计算效率，大大降低用户与服务器之间的通信成本和交互次数，并进行正确的特征关键点检测、准确的特征点描述和图像匹配。我们评估了我们新的安全 SIFT 方案的安全性和效率，并将我们的新安全 SIFT 方案与其他方案进行了详细比较。结果表明它最接近原始的SIFT算法。并详细比较我们新的安全 SIFT 方案与其他方案。结果表明它最接近原始的SIFT算法。并详细比较我们新的安全 SIFT 方案与其他方案。结果表明它最接近原始的SIFT算法。