分布式用户隐私保护可调节的云服务个性化QoS预测模型

许建龙; 林健; 黎宇森; 熊智

doi:10.11959/j.issn.2096-109x.2023022

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分布式用户隐私保护可调节的云服务个性化QoS预测模型

学术论文 | 更新时间：2024-06-03

- 分布式用户隐私保护可调节的云服务个性化QoS预测模型
- Distributed user privacy preserving adjustable personalized QoS prediction model for cloud services
- 网络与信息安全学报 2023年9卷第2期页码：70-80
- 作者机构：
- 作者简介：
  
  [ "许建龙（1982- ），男，广东汕头人，汕头大学讲师，主要研究方向为隐私计算、服务计算、区块链" ]
  [ "林健（1996- ），男，福建莆田人，汕头大学硕士生，主要研究方向为隐私计算、服务计算" ]
  [ "黎宇森（2000- ），男，广东佛山人，主要研究方向为隐私计算、服务计算" ]
  [ "熊智（1978- ），男，湖北黄冈人，汕头大学教授，主要研究方向为信息安全、分布式计算" ]
- 基金信息：
  
  国家自然科学基金;TheNational Natural Science Foundation of China(61702318);广东省基础与应用基础研究基金;Guangdong Province Basic and Applied Basic Research Fund(2023A1515010707);广东省基础与应用基础研究基金;Guangdong Province Basic and Applied Basic Research Fund(2021A1515012527);广东省科技专项资金（“大专项+任务清单”）项目;Guangdong Science and Technology Special Fund Project （“Major Project + Task List”）(STKJ2021201);广东省普通高校重点领域专项;Special Projects in Key Fields of Guangdong Universities(2022ZDZX1008)
- DOI：10.11959/j.issn.2096-109x.2023022
  中图分类号： TP309
- 网络首发：2023-04，
  
  纸质出版：2023-04-25
- 稿件说明：
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许建龙, 林健, 黎宇森, 等. 分布式用户隐私保护可调节的云服务个性化QoS预测模型[J]. 网络与信息安全学报, 2023,9(2):70-80.

Jianlong XU, Jian LIN, Yusen LI, et al. Distributed user privacy preserving adjustable personalized QoS prediction model for cloud services[J]. Chinese Journal of Network and Information Security, 2023, 9(2): 70-80.
许建龙, 林健, 黎宇森, 等. 分布式用户隐私保护可调节的云服务个性化QoS预测模型[J]. 网络与信息安全学报, 2023,9(2):70-80. DOI： 10.11959/j.issn.2096-109x.2023022.

Jianlong XU, Jian LIN, Yusen LI, et al. Distributed user privacy preserving adjustable personalized QoS prediction model for cloud services[J]. Chinese Journal of Network and Information Security, 2023, 9(2): 70-80. DOI： 10.11959/j.issn.2096-109x.2023022.

摘要

个性化服务质量（QoS，quality of service）预测是构建高质量云服务系统的重要环节，传统基于协同过滤方法采用集中式的训练模式难以保护用户隐私，为了在获取高准确预测效果的同时有效保护用户隐私，提出分布式用户隐私保护可调节的云服务个性化QoS预测模型（DUPPA）。该模型采用“服务器-多用户”架构，服务器协调多个用户，处理多用户上传模型梯度和下载全局模型的请求并维护全局模型参数。为进一步保护用户隐私，提出用户隐私程度调节策略，通过调节本地模型参数初始化比例、梯度上传比例以平衡隐私程度和预测精度。在本地模型初始化阶段，用户计算本地模型与全局模型的差值矩阵，并选择差值矩阵中数值较大元素所对应的全局模型参数初始化本地模型参数；在梯度上传阶段，用户可选择部分重要的梯度上传至服务器来满足不同应用场景对隐私保护的需求。为了评估 DUPPA 的隐私程度，提出针对分布式矩阵分解模型梯度共享方案的数据重构攻击方法。实验结果表明，当DUPPA在梯度上传比例为0.1、本地模型参数初始化比例为0.5时，预测的平均绝对误差（MAE，mean absolute error）和均方根误差（RMSE， root mean square error）较传统的集中式矩阵分解模型分别降低了1.20%和0.91%；当DUPPA的梯度上传比例为0.1时，隐私程度至少是梯度上传比例为1时的5倍；当DUPPA的本地模型参数初始化比例为0.5时，隐私程度至少是本地模型参数初始化比例为1时的3.44倍。

Abstract

Personalized quality of service （QoS） prediction is crucial for developing high-quality cloud service system.However

the traditional collaborative filtering method

based on centralized training

presents challenges in protecting user privacy.In order to effectively protect user privacy while obtaining highly accurate prediction effect

a distributed user privacy adjustable personalized QoS prediction model for cloud services （DUPPA） was proposed.The model adopted a “server-multi-user” architecture

in which the server coordinated multiple users

handled multiple users’ requests for uploading model gradients and downloading global model

and maintained global model parameters.To further protect user privacy

a user privacy adjustment strategy was proposed to balance privacy and prediction accuracy by adjusting the initialization proportion of local model parameters and gradient upload proportion.In the local model initialization stage

the user calculated the difference matrix between the local model and the global model

and selected the global model parameters corresponding to the larger elements in the difference matrix to initialize the local model parameters.In the gradient upload stage

the user can select some important gradients to upload to the server to meet the privacy protection requirements of different application scenarios.To evaluate the privacy degree of DUPPA

a data reconstruction attack method was proposed for the distributed matrix factorization model gradient sharing scheme.The experimental results show that when DUPPA sets the gradient upload proportion to 0.1 and the local model parameter initialization proportion to 0.5

the predicted MAE and RMSE are reduced by 1.27% and 0.91%

respectively

compared with the traditional centralized matrix factorization model.Besides

when DUPPA sets the gradient upload proportion to 0.1

the privacy degree is 5 times higher than when the gradient upload proportion is 1.And when DUPPA sets the local model parameter initialization proportion to 0.5

the privacy degree is 3.44 times higher than when the local model parameter initialization proportion is 1.

关键词

Keywords

references

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