With the rapid development of mobile smartphone and its built-in location-aware devices, people are possible to establish trust relationships and further interaction with each other based their matched interests, hobbies, experiences, or spatiotemporal profiles. However, the possibility of sensitive information leakage and heavy computation overhead constrain the widespread use of the matching schemes in mobile social networks. Many privacy-preserving matching schemes were proposed recently years, but how to achieve privacy-preserving spatiotemporal matching exactly and efficiently remains an open question. In this paper, the authors propose a novel spatiotemporal matching scheme. The overlapping grid system is introduced into the scheme to improve the accuracy of spatiotemporal matching, and many repetitive records in a user's spatiotemporal profile are counted as one item so as to cut down the computation overhead. Their scheme decreases the spatiotemporal matching error, and promotes the efficiency of private matchmaking simultaneously. Thorough security analysis and evaluation results indicate that our scheme is effective and efficient.

With the rapid development of cloud storage and cloud computing technology, users tend to store data in the cloud for more convenient services. In order to ensure the integrity of cloud data, scholars have proposed cloud data integrity verification schemes to protect users’ data security. The storage environment of the Internet of Things, in terms of big data and medical big data, demonstrates a stronger demand for data integrity verification schemes, but at the same time, the comprehensive function of data integrity verification schemes is required to be higher. Existing data integrity verification schemes are mostly applied in the cloud storage environment but cannot successfully be applied to the environment of the Internet of Things in the context of big data storage and medical big data storage. To solve this problem when combined with the characteristics and requirements of Internet of Things data storage and medical data storage, we designed an SM2-based offline/online efficient data integrity verification scheme. The resulting scheme uses the SM4 block cryptography algorithm to protect the privacy of the data content and uses a dynamic hash table to realize the dynamic updating of data. Based on the SM2 signature algorithm, the scheme can also realize offline tag generation and batch audits, reducing the computational burden of users. In security proof and efficiency analysis, the scheme has proven to be safe and efficient and can be used in a variety of application scenarios.

Cloud storage technology is evolving at a high speed; effectively auditing the cloud data’s integrity has become a focal point. Recently, Ming and Shi proposed a certificateless integrity auditing scheme with a privacy protection function. The scheme used the certificateless cryptosystem to solve the certificate management problem of the auditing schemes based on public key infrastructure and the key escrow problem of the identity-based auditing schemes. Although their scheme is novel and efficient, we found that their scheme was not secure and could not achieve integrity auditing of cloud data. The malicious cloud server can generate the proof through the blocks and tags sent by the user. On the basis of the original scheme, we propose an improved auditing scheme; our new scheme is more secure and effective. In addition, for the problem of idle tags in the existing cloud data integrity auditing scheme, we propose the idea of intermediate tags and we applied the idea to the improved scheme to improve audit efficiency.