Recommendation system based on localized differential privacy

A recommendation system and differential privacy technology, applied in the field of privacy-protected recommendation systems, can solve security and efficiency issues, complex encryption and decryption calculations on the client and server sides, and achieve the effect of ensuring accuracy and protecting personal privacy

Active Publication Date: 2020-10-13
BEIHANG UNIV
View PDF4 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The recommendation system using encryption technology requires complex encryption and decryption calculations on the client and ser

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Recommendation system based on localized differential privacy
  • Recommendation system based on localized differential privacy
  • Recommendation system based on localized differential privacy

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0038] 1. System initialization

[0039] According to the privacy protection strength expected by the user, set a global system privacy budget ε, the recommended range is 0.5≤ε≤2.0, this parameter is used to balance the privacy provided by the system and the accuracy of the system. When ε is larger, the privacy protection strength provided by the system will be weaker, and the accuracy of the system will be higher; when ε is smaller, the privacy protection strength provided by the system will be stronger, and correspondingly, the accuracy of the system will be lower .

[0040] Another parameter L of the system (the user's maximum behavior sequence length) needs to be set according to the actual system needs. The criterion for setting this parameter is that the length L should be less than 90% of the user's actual behavior sequence length. In...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a recommendation system based on localized differential privacy, and the main process comprises the following steps that: (1) each user side encodes related historical commodity purchasing behavior data, then disturbs the data by using a random disturbance algorithm meeting the localized differential privacy, and finally sends the disturbed data to a server side; (2) the server side collects all disturbed data, then reconstructs relevant information of historical purchase behaviors of the user, and finally maps the relevant information into a low-dimensional vector by using a graph embedding algorithm and returns the low-dimensional vector to the user side; (3) each user side calculates a respective recommended candidate set through the finally generated vector, thereby finishing commodity recommendation under the condition of privacy protection. According to the invention, a localized differential privacy protection technology is used, and the system does not need a trusted third party as a medium, thereby guaranteeing that the privacy information of each user is not stolen by the third party, and also guaranteeing the usefulness of a final recommendation result of the system.

Description

technical field [0001] The invention relates to a recommendation system for privacy protection, in particular to a recommendation system based on localized differential privacy. Background technique [0002] Local Differential Privacy (Local Differential Privacy) is a privacy protection technology that does not require a trusted third party and can be applied to the protection of individual privacy data in data analysis. In localized differential privacy, each individual who needs privacy protection uses a random perturbation algorithm that satisfies the localized differential privacy mechanism to perturb their own data; data analysts analyze the relevant statistics of the original individual data by collecting perturbed data feature. Localized differential privacy provides parameters for controlling the strength of privacy, through which the security and usability of the random perturbation algorithm can be balanced. [0003] Graph Embedding (Graph Embedding) is to map th...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q30/06G06F21/62G06F17/18
CPCG06Q30/0631G06F21/6245G06F17/18
Inventor 刘傲姚燕青程显富
Owner BEIHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap