Resolve the classification problem on secure encrypted relational data

Data mining is a powerful new technique to discover knowledge within the large amount of the data. A number of theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, data owners now have the opportunity to outsource not only their data but also data processing functionalities to the cloud. Because of data security and personal privacy concerns, sensitive data (e.g., medical records) should be encrypted before being outsourced to a cloud, and the cloud should perform query processing tasks on the encrypted data only. These tasks are termed as Privacy Preserving Query Processing (PPQP) over encrypted data. These protocols protect the confidentiality of the stored data, user queries, and data access patterns from cloud service providers and other unauthorized users. Several queries were considered in an attempt to create a well-defined scope. These queries included the k-Nearest Neighbor (kNN) query, advanced analytical query, and correlated range query. This paper presents protocols utilize an additive cryptography base privacy preserving data mining technique at different stages of query processing to achieve the best performance all computations can be done on the encrypted data.