In the current HDFS（Hadoop Distributed File System） key management system, the encryption zone keys are all loaded into the memory during startup of key service. With the increase of the key resource, the occupied memory space also grows, bringing the bottleneck of memory space and key indexing. There are three challenges induced: how to organize cached data and efficiently handle queries with missed keys, how to adjust key resources in the cache, and how to accurately predict the use of keys. In order to achieve fine-grained and efficient caching and improve the efficiency of key use, key caching optimization was considered from three aspects: key index data structure, key replacement algorithm, and key prefetching strategy. An architecture of key cache replacement module was designed, and then a key replacement algorithm based on the query frequency was set. Specifically, from the perspective of heat computing and key replacement, the potential influencing factors affecting the popularity of key cache were analyzed which considered the file system and user of key management system. Besides, the basic model of key usage popularity was constructed. The hash table and minheap linked list was combined to maintain the heat of the key in use, and the elimination algorithm was set based on heat identification. The key in the cache was dynamically updated, and key usage was adjusted by the time controller, so as to realize key replacement according to the key heat. For key prefetching, key usage rules were obtained through log mining and periodical usage analyzing of key provisioning policies, which considered business processes and the time period dimension of user accessing. Experimental results show that the proposed key replacement algorithm can effectively improve the hit rate of cache queries, reduce memory usage, and ameliorate the impact of key file I/O interaction on query performance.

The big data environment presents the characteristics of multi-user cross-network cross-access, multi-service collaborative computing, cross-service data flow, and complex management of massive files.The existing access control models and mechanisms are not fully applicable for big data scenarios.In response to the needs of fine-grained access control and multi-service strategy normalization for cryptographic data in the big data environment, starting from the scene elements and attributes of access control, the HDFS-oriented CKCM was proposed by mapping the cyberspace-oriented access control (CoAC) model.Subsequently, a fine-grained access control management model for HDFS was proposed, including management sub-models and management supporting models.The Z-notation was used to formally describe the management functions and management methods in the management model.Finally, the CKCM system was implemented based on XACML to realize fine-grained secure access control for managing file and secret keys in HDFS.

Cross-domain access control can improves interoperability but reduces intra-domain autonomy.To balance inter-domain interoperability and intra-domain autonomy,a cross-domain access control policy mapping to the problem of multi-objective integer optimization programming was formulated.Both the maximization of inter-domain interoperability and the minimization of intra-domain autonomy were taken as the objectives.Further,seven constraints were designed to prevent typical cross-domain conflicts.To solve the optimization problem,a constrained NSGA-III algorithm was proposed.The experimental results show that the proposed algorithm can quickly converge and accurately find the policy mapping even in the large-scale datasets.

To address the problem that as preventing data leakage on Hadoop platform,the existing encryption schemes suffer from several problems (e.g.,single encryption algorithm,complicated key management,low encryption performance) and they cannot protect data stored in Hadoop effectively,a high-performance encryption and key management scheme for Hadoop was proposed.Firstly,a three-level key management system was extended with the domestic commercial cipher algorithm.Then,a new data structure for encryption zone key to reduce time consumption was designed.Finally,the computing process of data stream in parallel was scheduled.The experimental results show that compared with the existing Hadoop schemes,the proposed scheme can improve the efficiency of key management,and can speed up file encryption.

The rise of e-commerce and other network services have greatly promoted the development of electronic bill service,while traditional data storage schemes can no longer satisfy the rapid access requirements of massive electronic bill data.To solve these problems,a hierarchical scalable storage architecture for massive electronic bill was proposed,which combined hash modular algorithm and consistent hash algorithm,and supported fast data locating.A horizontal expansion scheme based on hash modular algorithm was designed to reduce the amount of data that needed to migrate when adding or deleting data nodes.Besides,a data caching scheme based on hot data and a load balancing scheme based on access delay were designed and implemented,further improving the speed of data access.Finally,the experiments prove the effectiveness of proposed architecture and schemes.

In order to make computationally weak cloud tenants can reconstruct a secret with efficiency and fairness,a cloud outsourcing secret sharing scheme was proposed,which combined cloud outsourcing computation with secret sharing scheme.In the process of outsourcing secret sharing,cloud tenants just need a small amount of decryption and validation operations,while outsource expensive cryptographic operations to cloud service provider (CSP).The scheme,without complex interactive augment or zero-knowledge proof,could detect malicious behaviors of cloud tenants or cloud service providers.And the scheme was secure against covert adversaries.Finally,every cloud tenant was able to obtain the secret fairly and correctly.Security analysis and performance comparison show that scheme is safe and effective.