云存储加密数据搜索技术研究
发布时间：2019-01-24 14:55
【摘要】：在云存储中,为了保护数据安全及用户隐私,通常需对数据加密。与明文不同,加密后的数据往往难以操作,除了数据属主之外,其他用户无法访问,这种限制严重影响了数据的可用性,数据的使用价值也大大降低。为了在保证数据安全及用户隐私的同时提高数据的可用性,本文针对云存储环境的特点,深入研究了LSH(Locality Sensitive Hashing)相似性搜索方法,并根据云存储海量数据的特点,在现有LSH方法的基础上对查询过程中的第二阶段加入伪相似对象剔除,提出了E-LSH(Efficient LSH)相似性搜索方法,在不影响准确率的前提下有效提高了查询效率,解决了加密造成的数据蔽塞。E-LSH方法采用索引与存储分离的方式,将特征项索引与密文数据分开存储,对特征项进行转码处理,用户在搜索数据过程中无法接触数据,既保护了用户的查询隐私,又保证了数据的安全。为了适用云存储海量数据搜索的需求,本文采用Map Reduce实现了E-LSH方法,并将其运行于Hadoop分布式环境,实验结果表明E-LSH方法与Multi-Probe LSH方法相比加速比达到18.4%。此外,本文还设计了一个云存储加密数据搜索系统方案,在保证数据安全的基础上实现了数据的搜索与安全共享。系统支持关键词和数据对象查询,系统构建者也可根据不同数据类型定制所需的系统,既满足了用户的数据搜索与共享需求,又方便了系统构建者和云存储服务提供商。
[Abstract]:In cloud storage, data is usually encrypted to protect data security and user privacy. Different from plaintext, encrypted data is often difficult to operate, except for the main data, other users can not access. This restriction seriously affects the availability of data, and the use value of data is greatly reduced. In order to improve the availability of data while ensuring data security and user privacy, the similarity search method of LSH (Locality Sensitive Hashing) is studied in depth according to the characteristics of cloud storage environment, and according to the characteristics of cloud storage mass data, On the basis of existing LSH methods, pseudo-similar objects are removed in the second stage of the query process, and a E-LSH (Efficient LSH) similarity search method is proposed, which can effectively improve the query efficiency without affecting the accuracy of the query. The method of E-LSH uses the method of separating index and storage, stores the index of feature item and ciphertext data separately, transcodes the feature item, and the user can not contact the data in the process of searching data. It not only protects the user's query privacy, but also ensures the security of the data. In order to meet the requirement of cloud storage mass data search, this paper uses Map Reduce to implement the E-LSH method and runs it in the distributed environment of Hadoop. The experimental results show that the acceleration ratio of the E-LSH method compared with the Multi-Probe LSH method is 18.4. In addition, this paper also designs a cloud storage encryption data search system, which realizes data search and security sharing on the basis of ensuring data security. The system supports keyword and data object query, and the system builder can customize the system according to different data types, which not only meets the data search and sharing needs of users, but also facilitates the system builder and cloud storage service provider.
【学位授予单位】：国防科学技术大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP333;TP309.7
，
本文编号：2414573
[Abstract]:In cloud storage, data is usually encrypted to protect data security and user privacy. Different from plaintext, encrypted data is often difficult to operate, except for the main data, other users can not access. This restriction seriously affects the availability of data, and the use value of data is greatly reduced. In order to improve the availability of data while ensuring data security and user privacy, the similarity search method of LSH (Locality Sensitive Hashing) is studied in depth according to the characteristics of cloud storage environment, and according to the characteristics of cloud storage mass data, On the basis of existing LSH methods, pseudo-similar objects are removed in the second stage of the query process, and a E-LSH (Efficient LSH) similarity search method is proposed, which can effectively improve the query efficiency without affecting the accuracy of the query. The method of E-LSH uses the method of separating index and storage, stores the index of feature item and ciphertext data separately, transcodes the feature item, and the user can not contact the data in the process of searching data. It not only protects the user's query privacy, but also ensures the security of the data. In order to meet the requirement of cloud storage mass data search, this paper uses Map Reduce to implement the E-LSH method and runs it in the distributed environment of Hadoop. The experimental results show that the acceleration ratio of the E-LSH method compared with the Multi-Probe LSH method is 18.4. In addition, this paper also designs a cloud storage encryption data search system, which realizes data search and security sharing on the basis of ensuring data security. The system supports keyword and data object query, and the system builder can customize the system according to different data types, which not only meets the data search and sharing needs of users, but also facilitates the system builder and cloud storage service provider.
【学位授予单位】：国防科学技术大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP333;TP309.7
，
本文编号：2414573
本文链接：https://www.wllwen.com/kejilunwen/jisuanjikexuelunwen/2414573.html
