Cpir-v Nearest Neighbor Privacy Preserving Query Method Based on Spark and Huffman Coding

Abstract

The invention discloses a CPIR-V nearest neighbor privacy-preserving query method based on Spark and Huffman coding. The CPIR-V nearest neighbor privacy-preserving query method based on Spark and Huffman coding comprises the following steps: compressing data of a nearest neighbor matrix by using Huffman coding to reduce data Bit in each grid; storing compressed data, the code length of characters and an element maximum value in an empty database HBase; reading data in the HBase database by using a server side, caching the data in RDD of a Spark parallel frame, grouping the CPIR nearest neighbor matrix in RDD according to a parallel strategy, carrying out CPIR parallel calculation according to query information by a Spark server side after grouping, aggregating calculating results of the groups, and then transmitting a query result and the code length of the characters to a client side; analyzing the query result by using the client side to obtain a value of a query bit, and decompressing the value of the query bit to obtain query information. By the privacy-preserving query algorithm based on Spark parallelization and Huffman coding, query privacy of a user is preserved and the query efficiency is improved on the basis of the original query effect in a big data application scene.

Description

technical field [0001] The invention relates to the technical field of communication networks, in particular to a CPIR-V nearest neighbor privacy protection query method based on Spark and Huffman coding. Background technique [0002] With the continuous development and production of mobile devices, the emergence of various positioning methods and various communication methods, due to the emergence of various positioning technologies, the popularization of mobile terminals and the wide use of communication equipment, location-based services (LBS) as the representative mobile applications have entered the era of mobile big data. It is not enough to deal with the increasing amount of data only by relying on the computing power of the existing PC and server organizational structure, but if the computing power is improved by upgrading the hardware equipment, a lot of financial and material resources will be wasted, and effective Horizontal scalability and maintainability. Ther...

AI Technical Summary

Problems solved by technology

The CPIR algorithm greatly reduces the complexity of communication, but also increases the complexity of calculation, ensuring the strongest degree of privacy protection

However, LBS privacy protection involves a large number of calculation operations and complex transformation operations. The CPIR algorithm needs to scan the entire data space during calculation, resulting in a large amount of calculation and a long calculation time, which makes the computing power of traditional computing platforms unable to meet the existing requirements. needs

Images