Due to countless orthogonal eigenstates, light beams with orbital angular momentum(OAM) have a large potential information capacity. Recently, deep learning has been extensively applied in recognition of OAM mode. However, previous deep learning methods require a constant distance between laser and receiver. The accuracy will drop quickly if the distance of testing set deviates from the training set. Previous deep learning methods also have difficulty distinguishing OAM modes with positive and negative topological charges. In order to further exploit the huge potential of the countless dimension of state space, we proposed multidimensional information assisted deep learning flexible recognition (MIADLFR) method to make use of both intensity and angular spectrum information for the first time to achieve recognition of OAM modes unlimited by the sign of TC and distance with high accuracy. Also, MIADLFR can reduce the computational complexity significantly and requires much smaller training set.

With the advent and development of database applications such as big data and data mining, how to ensure the availability of data without revealing sensitive information has been a significant problem for database privacy protection. As a critical technology to solve this problem, homomorphic encryption has become a hot research area in information security at home and abroad in recent years. The paper sorted out, analyzed, and summarized the research progress of homomorphic encryption technology in database privacy protection. Moreover, the application of three different types of homomorphic encryption technology in database privacy protection was introduced respectively, and the rationale and characteristics of each technique were analyzed and explained. Ultimately, this research summarized the challenges and development trends of homomorphic encryption technology in the application of database privacy protection, which provides a reference for future research.

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