Research Advances on Big Data Security and Privacy Preserving

Nowadays, data security and privacy preserving have been definitely becoming one of the most crucial issues in the big data setting, where data encryption plays the most important role to achieve these goals. Therefore, to explore new data encryption techniques and new modes of big data processing has emerged as one of the most popular research topics all over the world. During the whole life cycle of data, the problems of computation, access control and data aggregation in the ciphertext domain (ciphertext computation, ciphertext access control and ciphertext data aggregation) are three critical issues in this research field. In this paper, we firstly review the state-of-the-art in the field of ciphertext computation, ciphertext access control and ciphertext data aggregation by identifying their inappropriateness. Based on it, a series of recent results in this research field are presented. In the aspect of ciphertext computation, a new method of designing efficient privacy preserving outsourced computation by reducing the usage times of public key encryption is proposed, with the implementation of a concrete construction which is realized by one time offline computation of any one-way trapdoor permutation without exploiting the technique of public key (fully) homomorphic encryption. In the aspect of ciphertext access control, a short ciphertext size traceable and revocable attribute-based encryption supporting flexible attributes is proposed. In the aspect of ciphertext data aggregation, an efficient privacy preserving data aggregation protocol with both input privacy and output privacy is devised without exploiting public key additive homomorphic encryption. Finally, we also suggest several interesting open research issues and the trend in the future.