With the popularization of cloud computing, many business and individuals prefer to outsource their data to cloud in encrypted form to protect data confidentiality. However, how to search over encrypted data becomes a concern for users. To address this issue, searchable encryption is a novel cryptographic primitive that enables user to search queries over encrypted data stored on an untrusted server while guaranteeing the privacy of the data. Public key encryption with keyword search (PEKS) has received a lot of attention as an important branch. In this paper, we focus on the development of PEKS in cloud by providing a comprehensive research survey. From a technological viewpoint, the existing PEKS schemes can be classified into several variants: PEKS based on public key infrastructure, PEKS based on identity-based encryption, PEKS based on attribute-based encryption, PEKS based on predicate encryption, PEKS based on certificateless encryption, and PEKS supporting proxy re-encryption. Moreover, we propose some potential applications and valuable future research directions in PEKS.

With the application of multimedia big data, the problems such as information leakage and data tampering have emerged. The security of images which is one of the most typical multimedia has become a major problem facing the large-scale open network environment. This paper proposed a blockchain-watermarking scheme to protect the privacy, integrity and availability of compressed sensed images, which effectively combines multimedia watermarking, compressed sensing, Interplanetary File System (IPFS) and blockchain technologies. Based on the reliable authentication of watermarking, the confidentiality protection of compressed sensing, the secure storage of IPFS, and the decentralization and non-tamperability of blockchain, the all-round security protection of the image big data based on compressive sensing can be realized. Experiments show that the proposed scheme is effective and feasible.

Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this paper, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.

Linear secret-sharing scheme (LSSS) is a useful tool for supporting flexible access policy in building attribute-based encryption (ABE) schemes. But in lattice-based ABE constructions, there is a subtle security problem in the sense that careless usage of LSSS-based secret sharing over vectors would lead to the leakage of the master secret key. In this paper, we propose a new method that employs LSSS to build lattice-based key-policy attribute-based encryption (KP-ABE) that resolves this security issue. More specifically, no adversary can reconstruct the master secret key since we introduce a new trapdoor generation algorithm to generate a strong trapdoor (instead of a lattice basis), that is, the master secret key, and remove the dependency of the master secret key on the total number of system attributes. Meanwhile, with the purpose of reducing the storage cost and support dynamic updating on attributes, we extended the traditional 1-dimensional attribute structure to 2-dimensional one. This makes our construction remarkably efficient in space cost, with acceptable time cost. Finally, our scheme is proved to be secure in the standard model.

Attribute-based encryption (ABE) is a good choice for one-to-many communication and fine-grained access control of the encryption data in a cloud environment. Fully homomorphic encryption (FHE) allows cloud servers to make valid operations on encrypted data without decrypting. Attribute-based fully homomorphic encryption (ABFHE) from lattices not only combines the bilateral advantages/facilities of ABE and FHE but also can resist quantum attacks. However, in the most previous ABFHE schemes, the growth of ciphertext size usually depends on the total number of system’s attributes which leads to high communication overhead and long running time of encryption and decryption. In this paper, based on the LWE problem on lattices, we propose an attribute-based fully homomorphic scheme with short ciphertext. More specifically, by classifying the system’s attributes and using the special structure matrix in MP12, we remove the dependency of ciphertext size on system’s attributes and the ciphertext size is no longer increased with the total number of system’s attributes. In addition, by introducing the function in the homomorphic operations, we completely rerandomize the error term in the new ciphertext and have a very tight and simple error analysis using sub-Gaussianity. Besides, performance analysis shows that when and according to the parameter suggestion given by Micciancio and Dai et al., the size of ciphertext in our scheme is reduced by at least 73.3%, not to mention . The larger the , the more observable of our scheme. The short ciphertext in our construction can not only reduce the communication overhead but also reduce the running time of encryption and decryption. Finally, our scheme is proved to be secure in the standard model.