Secure outsourcing SIFT: Efficient and Privacy-Preserving Image Feature Extraction in the Encrypted Domain

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.

1 Introduction

With the development of cloud computing, server provides huge storage space and powerful computing capacity. For users and enterprises, server not only stores a variety of text files, but also stores a variety of multimedia files (images, voices and videos). Cloud service provides us with convenience, but server is not always credible. When malicious users access to server, data on server may expose privacy of users and enterprises [1], [2], such as sex of user, hobbies, home address and workplace, looks, salary, and so on. Privacy and security of cloud computing have become research hotspots. To preserve privacy and insure security for cloud computing, one of the best opinions is to encrypt data. Then, the encrypted data is uploaded to server by users. All operations of server are performed on encrypted data. Server can perform computing on encrypted data without decryption [3], [4]. Fu et al. proposed a content-aware search scheme which can make semantic search more smart. Their scheme uses conceptual graphs as a knowledge representation to substitute traditional keywords and solve the problem of privacy-preserving smart semantic search over encrypted outsourced data [5]. Many schemes support privacy-preserving keyword search [6], [7], [8] on text data. Server does not know contents of queries and the returned results on queries. Privacy of users and users’ query is protected.