基于分支混淆算法的隐私数据库自适应加密方法

为有效降低隐私数据库中数据泄露的可能性，进一步提高数据安全性，本文设计一种基于分支混淆算法的隐私数据库自适应加密方法。在收集应用程序对数据库的请求内容的基础上，利用分支混淆算法对隐私数据库内数据的特征进行分类，并选取朴素贝叶斯分类器对数据库内混淆点实施分类，从而确定数据库中数据的字段类型,基于此，针对不同字段类型数据选取相应加密算法。其中，数值类型数据采用保序加密算法，等值比较类型数据采用等值加密算法，文本类型数据采用分词辅助索引加密算法。在加密数值型、时间类型、字符型字段的同时，保留偏序特性和可检索特性。实验结果表明提出的方法可实现对隐私数据库内不同字段类型数据的有效加密，且加密所需的时间开销较小，安全性高。

Adaptive Encryption Method for Private Database Based on Branch Obfuscation Algorithm

In order to effectively reduce the possibility of data leakage in private database and further improve data security, an adaptive encryption method for private database based on branch obfuscation algorithm is designed in this paper. On the basis of collecting the request contents of the application program to the database, the characteristics of the data in the privacy database are classified by using the branch obfuscation algorithm, and the confusion points in the database are classified by selecting the Naive Bayes classifier, so as to determine the field types of the data in the database. Based on this, data encryption algorithms are selected for different field types. Among them, the sequence-preserving encryption algorithm is used for the numerical data, the equivalent encryption algorithm is used for the equivalent comparison data, and the word segmentation assisted index encryption algorithm is used for the text data. While encrypting numeric, time and character fields, the partial order and searchable properties are preserved. The experimental results show that the proposed method can effectively encrypt the data of different field types in the privacy database, and the encryption requires less time and has high security.