Anonymization General Process and Risk Assessment Method for Data Compliance

doi:10.3969/j.issn.1671-1122.2024.12.005

Abstract

Abstract:

The leakage of personal privacy has emerged as a critical challenge in data security. Anonymization can effectively reduce the risk of privacy leakage by deidentification of personal information. However, inappropriate data processing methods can affect the results. Moreover, a residual risk of re-identification remains after data release. As domestic security supervision on data circulation intensifies, it is of great significance for personal information sharing to establish a reasonable anonymization process and assess the residual risks of anonymized data under data compliance. The previous anonymous risk assessment primarily center on evaluating data security through attack models. Additionally, these studies often overlook inherent risks within the anonymous process itself and the compliance of anonymous data. Therefore, this article introduced an anonymization general process. Building upon it, a risk assessment around data security and compliance was devised. The risk assessment method focused on process risk and data re-identification risk. It contained a supporting evaluation method and index system. In compliance evaluation, this article summarized existing standards. It proposed quantifiable compliance requirements to ensure compliance while assessing data risks. Finally, this article conducted a simulation experiment of anonymous process to verify process feasibility. The experimental result verifies that the risk assessment method can effectively detect potential threats in anonymization by simulating different risk scenarios.

Key words: data compliance, anonymization general process, anonymization risk assessment, process risk, re-identification risk

CLC Number:

TP309

YUAN Yulin, YUAN Shuguang, YU Jing, CHEN Chi. Anonymization General Process and Risk Assessment Method for Data Compliance[J]. Netinfo Security, 2024, 24(12): 1855-1870.

Figures/Tables 21

References 48

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数据分级	发布方式	风险阈值	特殊要求
非敏感个人信息 L1	完全公开发布	≤40%	无
低敏感个人信息 L2	受控公开发布	≤25%	（1）生物识别信息仅可用于科研场景发布；（2）行踪轨迹信息不应包含国家敏感区域位置；（3）医疗健康信息仅可用于临床研究、医药/医疗研发、内部培训、学术研讨场景，且不可包含医护人员姓名
较敏感个人信息 L3		≤15%
高敏感个人信息 L4		≤10%

标度	含义
1	准则C_i和准则C_j同等重要
3	准则C_i相比准则C_j略微重要
5	准则C_i相比准则C_j明显重要
7	准则C_i相比准则C_j强烈重要
9	准则C_i相比准则C_j绝对重要
2,4,6,8	上述两个判断之间的中值
以上数值的倒数	若准则C_i相比准则C_j标度为上述非零数之一，则准则C_j相比准则C_i标度值为其倒数

指标	I₅	I₆	I₇	I₈
I₅	a_5,5	a_5,6	a_5,7	a_5,8
I₆	a_6,5	a_6,6	a_6,7	a_6,8
I₇	a_7,5	a_7,6	a_7,7	a_7,8
I₈	a_8,5	a_8,6	a_8,7	a_8,8

指标	特征向量	权重值	最大特征根	C.I.值
I₅	$\overline{{{w}_{5}}}$	${{w}_{5}}$	${{\lambda }_{\max }}$	$\frac{{{\lambda }_{\max }}-n}{n-1}$
I₆	$\overline{{{w}_{6}}}$	${{w}_{6}}$
I₇	$\overline{{{w}_{7}}}$	${{w}_{7}}$
I₈	$\overline{{{w}_{8}}}$	${{w}_{8}}$

指标	I₅	I₆	I₇	I₈
I₅	1	1/3	1/3	1/2
I₆	3	1	1	2
I₇	3	1	1	2
I₈	2	1/2	1/2	1