Differential privacy federal modeling method and device for resisting inference attack of semi-honest server

An inferred attack and differential privacy technology, which is applied in the field of differential privacy federated modeling, can solve the problems of inability to resist inferred attacks and low precision of differential privacy models, and achieve the effect of improving the ability to infer attacks, improve accuracy, and improve security

Pending Publication Date: 2022-07-29
WUHAN UNIV OF TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The present invention proposes a differential privacy federated modeling method and device for resisting semi-honest server inference attacks, to solve or at le

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  • Differential privacy federal modeling method and device for resisting inference attack of semi-honest server
  • Differential privacy federal modeling method and device for resisting inference attack of semi-honest server
  • Differential privacy federal modeling method and device for resisting inference attack of semi-honest server

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Embodiment 1

[0049] Embodiments of the present invention provide a differential privacy federation modeling method for resisting semi-honest server inference attacks, including:

[0050] S1: Initialize the global model;

[0051] S2: The semi-honest central server selects a number of data participants to participate in this round of federated training according to a preset probability;

[0052] The data participant selected in S3 downloads the global model, and flexibly fuses the local model trained in the previous round with the global model to obtain the elastically fused local model;

[0053] S4: The selected data participants use local data to perform local private training on the elastically fused local model, obtain the local model after noise disturbance, and then send it to the semi-honest central server;

[0054] S5: The semi-honest central server aggregates the local models perturbed by noise to obtain a perturbed global model.

[0055] Specifically, steps S2 to S5 are performed...

Embodiment 2

[0107] Based on the same inventive concept as the first embodiment, this embodiment provides a differential privacy federation modeling apparatus for resisting semi-honest server inference attacks, including:

[0108] The initialization module is used to initialize the global model;

[0109] The data participant selection module is used to select a number of data participants to participate in this round of federated training according to a preset probability;

[0110] The elastic fusion module is used to download the global model, and flexibly fuse the local model trained in the previous round with the global model to obtain the elastically fused local model;

[0111] The local secret training module is used to perform local private training on the elastically fused local model by using local data, obtain the local model after noise disturbance, and then send it to the semi-honest central server;

[0112] The aggregation module is used to aggregate the local models perturbed...

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Abstract

The invention provides a differential privacy federated modeling method and device for resisting inference attack of a semi-honest server. The method comprises the following steps: initializing a global model; the semi-honesty central server selects a plurality of data participants to participate in the federation training according to a preset probability; downloading the global model by the selected data participant, and performing elastic fusion on the local model trained in the previous round and the global model to obtain a local model after elastic fusion; the selected data participant performs local private training on the elastically fused local model by using local data to obtain a local model after noise disturbance, and then sends the local model to a semi-honest central server; and the semi-honesty central server aggregates the local models after noise disturbance to obtain a global model of disturbance. According to the method, the robustness in the joint modeling process can be improved, and the precision of the model can also be improved.

Description

technical field [0001] The invention relates to the technical field of privacy-enhanced federated learning for multi-party joint deep learning modeling, in particular to a differential privacy federated modeling method and device for resisting semi-honest server inference attacks. Background technique [0002] Data has become a basic production factor for information circulation and value transfer. How to promote the circulation and sharing of data on the premise of protecting privacy is a major challenge currently facing. Federated learning is a new paradigm of machine learning and deep learning model training. It keeps model training and data storage at the edge of the distributed network. Its privacy protection ability shows its application value in many business scenarios. It enables all participants to join forces without sharing data resources, that is, to establish a sharing model without the need to share data locally, and effectively solve the problems of data priva...

Claims

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Application Information

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IPC IPC(8): G06F21/56G06K9/62G06N3/04G06N3/08
CPCG06F21/56G06N3/04G06N3/08G06F18/214
Inventor 陆丽萍朱锦雄熊盛武闻源陆林
Owner WUHAN UNIV OF TECH
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