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Distributed differential privacy aggregation method

A technology of differential privacy and aggregation method, applied in the direction of platform integrity maintenance, digital transmission system, electrical components, etc., can solve the problems of inability to achieve centralized differential privacy accuracy, efficiency loss, etc., to improve use efficiency and accuracy , a wide range of effects

Active Publication Date: 2021-08-24
NANKAI UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, although it is currently proposed to achieve the effects of privacy amplification and error reduction through localized scheme design or adding shuffling operations, the implementation based on localized differential privacy mechanisms still cannot achieve the accuracy of centralized differential privacy
In addition, the guarantee of verifiability of the shuffling operation has a certain degree of efficiency loss

Method used

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  • Distributed differential privacy aggregation method
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Embodiment Construction

[0032] In order to explain in detail the technical content, structural features, achieved goals and effects of the technical solution, the following will be described in detail in conjunction with specific embodiments and accompanying drawings.

[0033] refer to Figure 4 Shown is the flow chart of distributed differential privacy data collection and secure aggregation. An accurate and efficient distributed differential privacy aggregation method, the specific steps of which include:

[0034] Step 1, such as Figure 4 As shown by arrow ①, before the aggregator group collects data, the client negotiates with the aggregator group on the parameters used, and then assigns a random number to each user through secure multi-party computation, which is used for the client to upload data securely during subsequent data collection used when.

[0035] Before collecting data, the aggregator group interacts once to obtain their respective random numbers through homomorphic encryption. ...

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Abstract

The invention belongs to the technical field of data collection of distributed privacy protection, and particularly relates to a distributed differential privacy aggregation method. The method comprises the following steps: distributed implementation is performed on a centralized differential privacy algorithm in a mode of adding noise at a user side on the basis that the centralized differential privacy algorithm obeys the additive property of the distribution; in the process that a user sends data and aggregates the data, by using a secure multi-party computing method, the user sends data randomized through a differential privacy algorithm to all aggregators in a secret sharing mode, and then all the aggregators interact to obtain a final randomized aggregation result. According to the accurate and efficient distributed differential privacy aggregation method provided by the invention, a differential privacy algorithm is realized in a distributed manner, so that the collection accuracy of distributed privacy protection data is improved, and under the trust hypothesis that a data collection party is not trusted, the security of intermediate data of a user is ensured through secure multi-party calculation, and the security data aggregation efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of data collection for distributed privacy protection, and specifically relates to a distributed differential privacy aggregation method, which includes distributed implementation of a centralized differential privacy algorithm and data aggregation based on secure multi-party computing. Background technique [0002] Differential privacy technology provides strict mathematical guarantees for personal privacy protection in statistical data. With the development of the big data era, the feedback of related usage and settings of terminal and mobile users is of great significance for improving user experience and detecting malicious behavior. [0003] The distributed privacy-preserving data collection method generally adopts localized differential privacy or its extension method. In this method, the data collector is untrustworthy, which is a more reasonable trust assumption for users, which can make users more ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L9/00H04L29/06G06F21/57
CPCH04L9/008H04L63/0428G06F21/57
Inventor 刘哲理吴玉铎贾靖宇郭晓杰
Owner NANKAI UNIV
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