Encrypted Scalar Product Protocol for Outsourced Data Mining

Abstract

Organizations and individuals nowadays face increasing daily operations closely rely on a huge amount of private data which is outsourced to a centralized server. Secure and efficient data processing and mining on such outsourced private data becomes a primary concern for users, especially with the push of cloud computing which has both resource and compute scalability. Among the building blocks of secure data mining algorithms, secure scalar product is used to calculate the sum of the products of the corresponding values of two vectors. Existing privacy preserving methods assume data is stored at the user side, and users follow a protocol to perform privacy preserving scalar product. However, such methods are not applicable as data now is outsourced to a centralized server in its encrypted form. To solve this problem, in this paper, we design a novel Protocol for Outsourced Scalar Product (POSP) that performs collaborative operations between server and users to produce the scalar product result without violating each user's data privacy. We proved that POSP can return the correct result and is secure. We also analysed that POSP has linear complexity in terms of space, computation, and communication with respect to the vector length.