Privacy protection fine-grained data aggregation method and system

Abstract

The invention discloses a privacy protection fine-grained data aggregation method and system. The method comprises accurate additive multi-subset data aggregation and approximate non-additive multi-subset data aggregation. The precise additive multi-subset data aggregation comprises an initialization stage, a user report generation stage, a privacy protection user report aggregation stage and a control center decryption stage. The approximate non-additive multi-subset data aggregation comprises non-additive data aggregation for all users and non-additive data aggregation for multiple subsets; accurate additive multi-subset data aggregation (such as accurate mean value, variance and one-way variance analysis aggregation) and approximate non-additive multi-subset data aggregation (such as approximate minimum aggregation and maximum aggregation) are supported, and therefore the diversity requirement of intelligent services is met.

Description

technical field [0001] The invention belongs to the technical field of information security, and relates to a privacy protection data aggregation method in applied cryptography, in particular to a multifunctional and high-performance privacy protection fine-grained data aggregation method and system. Background technique [0002] Smart grid integrates information and communication technology into traditional power network to form an efficient, reliable and safe power system. As an important part of it, the advanced metering infrastructure (AMI) network allows the control center to collect fine-grained electricity consumption data of residential customers every few minutes for energy management and real-time load monitoring. For example, by analyzing and monitoring these fine-grained power consumption data, the control center can adjust the power production plan, thereby reducing the risk of power outages. [0003] Although it is valuable to analyze these fine-grained electr...

AI Technical Summary

Problems solved by technology

However, most of these schemes only focus on privacy-preserving multi-subset data aggregation methods for specific functions (such as averaging, variance, etc.), which are difficult to meet the diverse needs of intelligent services.

To the best of our knowledge, there is no formal research on multifunctional PPMDA (i.e., capable of averaging, variance, one-way ANOVA, max, min privacy-preserving multi-subset data aggregation) in AMI networks

In addition, when most existing PPMDA schemes aggregate privacy-preserving data, users often only perform a single specific function aggregation operation after uploading a set of data, which cannot make full use of the data, which is not efficient enough

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