Aiming at the problems of limited attack rounds and high attack complexity of Blow-CAST-Fish （Blow-C.Adams S.Tavares-Fish） algorithm， a key recovery attack of Blow-CAST-Fish algorithm based on differential table was proposed. Firstly， after analyzing the collision of S-box， based on the collision of two S-boxes and a single S-box respectively， the 6-round and 12-round differential characteristics were constructed. Secondly， the differential tables of f₃ were calculated， and three rounds were expanded based on the specific differential characteristic， thereby determining the relationship between ciphertext difference and the input and output differences of f₃. Finally， the plaintexts meeting the conditions were selected to encrypt， the input and output differences of f₃ were calculated according to the ciphertext difference， and the corresponding input and output pairs were found by querying the differential table， as a result， the subkeys were obtained. At the situation of two S-boxes collision， the proposed attack completed a differential attack of 9-round Blow-CAST-Fish algorithm， compared with the comparison attack， the number of attack rounds was increased by one， and the time complexity was reduced from 2^107.9 to 2⁷⁴. At the situation of single S-box collision， the proposed attack completed a differential attack of 15-round Blow-CAST-Fish algorithm， compared with the comparison attack， although the number of attack rounds was reduced by one， the proportion of weak keys was increased from {\mathrm{2}}^{-\mathrm{52.4}} to {\mathrm{2}}^{-\mathrm{42}} and the data complexity was reduced from 2⁵⁴ to 2⁴⁷. The test results show that the attack based on differential table can increase the efficiency of attack based on the same differential characteristics.

The manual labeling of abundant data is laborious and the amount of Magnetic Resonance Imaging （MRI） data from a single imaging center is limited. Concerning the above problems， a Magnetic Resonance Semi-Supervised Learning （MRSSL） method utilizing multi-imaging center labeled and unlabeled MRI data was proposed and applied to knee abnormality classification. Firstly， data augmentation was used to provide the inductive bias required by the model . Next， the classification loss and the consistency loss were combined to constraint an artificial neural network to extract the discriminative features from the data. Then， the features were used for the MRI knee abnormality classification. Additionally， the corresponding Magnetic Resonance Supervised Learning （MRSL） method only using labeled samples was proposed and compared with MRSSL for the same labeled samples. The results demonstrate that MRSSL surpasses MRSL in both model classification performance and model generalization ability. Finally， MRSSL was compared with other semi-supervised learning methods. The results indicate that data augmentation plays an important role on performance improvement， and with stronger inclusiveness for MRI data， MRSSL outperforms others on the knee abnormality classification.

For retrieving the encrypted data in cloud environment quickly， an efficient searchable encryption scheme for batch data processing scenarios was proposed. Firstly， two inverted indexes were built by the client， one file index was used to store the file-keyword mapping， another empty search index was used to store keyword-file mapping. Then， these two indexes were submitted to the cloud server. The search index_{was gradually updated and constructed according to the search tokens and file indexesduring the user’s search by the cloud， and the search results of the searched keywords were recorded by this search index. In this way， the search index construction time was shared to each retrieval process effectively and the storage space of search index was reduced. A set storage method based on key-value structure was adopted by the indexes， which supported the at-the-same-time merging and splitting of index， which means when adding and deleting files， the corresponding file index and search index were generated by the client according to the file set to be added or deleted， then the server merged or split the indexes， so that the files were able to be added and deleted in batch quickly. Testing results show that the proposed scheme greatly improves the updating efficiency of files and is suitable for batch data processing. Through leakage function， it is proved that the proposed scheme can meet the indistinguishability security standard against adaptive dynamic keyword selection attack.}