A survey on key technologies of privacy protection for machine learning

Zishan LIU; Qiang CHENG; Bo LV

doi:10.11959/j.issn.1000-0801.2020283

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A survey on key technologies of privacy protection for machine learning

Comprehensive Review | 更新时间：2024-06-05

- A survey on key technologies of privacy protection for machine learning
- Telecommunications Science Vol. 36, Issue 11, Pages: 18-27(2020)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020283
  CLC： TP181
- Published Online：2020-11，
  
  Published：20 November 2020
- 稿件说明：
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Zishan LIU, Qiang CHENG, Bo LV. A survey on key technologies of privacy protection for machine learning[J]. Telecommunications science, 2020, 36(11): 18-27.
DOI：

Zishan LIU, Qiang CHENG, Bo LV. A survey on key technologies of privacy protection for machine learning[J]. Telecommunications science, 2020, 36(11): 18-27. DOI： 10.11959/j.issn.1000-0801.2020283.

摘要

随着信息通信技术的发展，机器学习已经成为多个研究领域与垂直行业必不可少的技术工具。然而，机器学习所需数据中往往包含了大量的个人信息，使其隐私保护面临风险与挑战，受到了越来越多的关注。对现有机器学习下隐私保护法规政策与标准化现状进行梳理，对适用于机器学习的隐私保护技术进行详细介绍与分析。隐私保护算法通常会对数据质量、通信开支与模型表现等造成影响，因此对于隐私保护算法的评估应当进行多维度的综合评估。总结了适用于机器学习应用的隐私保护性能评估指标，并指出隐私保护需要考虑对数据质量、通信开支以及模型准确率等之间的影响。

Abstract

With the development of information and communication technology

large-scale data collection has vastly promoted the application of machine learning in various fields.However

the data involved in machine learning often contains a lot of personal private information

which makes privacy protection face new risks and challenges

and has attracted more and more attention.The current progress of the related laws

regulations and standards to the personal privacy protection and data safety in machine learning were summarized.The existing work on privacy protection for machine learning was presented in detail.Privacy protection algorithms usually have influence on the data quality

model performance and communication cost.Thus

the performance of the privacy protection algorithms should be comprehensively evaluated in multiple dimensions.The performance evaluation metrics for the privacy protection algorithms for machine learning were presented

given with the conclusion that the privacy preservation on machine learning needs to balance the data quality

model convergence rate and communication cost.

关键词

Keywords

references

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