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Information security technology - Guide for evaluating the effectiveness of personal information de-identification
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GB/T 42460-2023: PDF in English (GBT 42460-2023) GB/T 42460-2023
GB
NATIONAL STANDARD OF THE
PEOPLE’S REPUBLIC OF CHINA
ICS 35.030
CCS L 80
Information security technology - Guide for evaluating the
effectiveness of personal information de-identification
ISSUED ON: MARCH 17, 2023
IMPLEMENTED ON: OCTOBER 01, 2023
Issued by: State Administration for Market Regulation;
Standardization Administration of the People's Republic of China.
Table of Contents
Foreword ... 3
Introduction ... 4
1 Scope ... 5
2 Normative references ... 5
3 Terms and definitions ... 5
4 Grading of personal information de-identification effectiveness ... 7
5 Evaluation process for effectiveness of personal information de-identification ... 8
6 Evaluation implementation ... 9
6.1 Evaluation preparation ... 9
6.2 Qualitative evaluation ... 10
6.3 Quantitative evaluation ... 10
6.4 Formation of evaluation conclusions ... 11
6.5 Communication and negotiation ... 11
6.6 Evaluation process documentation management ... 11
Annex A (informative) Examples for direct identifiers ... 13
Annex B (informative) Examples for quasi-identifiers ... 14
Annex C (informative) Identification of quasi-identifier ... 15
Annex D (informative) Examples for de-identification effectiveness evaluation based
on K-anonymity model ... 17
Bibliography ... 25
Information security technology - Guide for evaluating the
effectiveness of personal information de-identification
1 Scope
This document provides guidelines for grading and evaluating the effectiveness of
personal information de-identification.
This document applies to personal information de-identification activities. It is also
applicable to personal information security management, supervision and evaluation.
2 Normative references
The following referenced documents are indispensable for the application of this
document. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
GB/T 25069-2022, Information security techniques -- Terminology
GB/T 35273-2020, Information security technology -- Personal information security
specification
GB/T 37964-2019, Information security technology -- Guide for de-identifying
personal information
3 Terms and definitions
For the purposes of this document, the terms and definitions defined in GB/T 25069-
2022, GB/T 35273-2020, GB/T 37964-2019 as well as the followings apply.
3.1 personal information
Various information related to identified or identifiable natural persons recorded
electronically or otherwise.
NOTE: Anonymized information is not included.
[Source: GB/T 35273-2020, 3.1, modified]
3.2 personal information subject
The natural person identified or associated with the personal information.
3.9 completely public sharing
Once the data is released, it is difficult to recall, and it is generally released directly
through the Internet.
[Source: GB/T 37964-2019, 3.12]
3.10 controlled public sharing
The use of data is constrained by the data use protocol.
[Source: GB/T 37964-2019, 3.13]
3.11 enclave public sharing
Share within physical or virtual jurisdictions. Data cannot be exported outside the
territory.
[Source: GB/T 37964-2019, 3.14]
3.12 re-identification risk; identifiability
The probability that the subject of personal information can be identified from the data.
3.13 equivalence class
A collection of rows in microdata where all quasi-identifier attribute values have the
same value.
3.14 acceptable risk threshold
The set re-identification risk threshold value.
NOTE: When the re-identification risk is greater than this value, mitigation measures (including
de-identification processing) and emergency measures need to be taken to keep the risk within a
controllable range.
4 Grading of personal information de-identification effectiveness
Based on whether the data can directly identify the subject of personal information, or
how likely it is to identify the subject of personal information, the identifiability of
personal information is graded into four levels, see Table 1, used to distinguish the
effectiveness of de-identification of personal information.
Table 1 -- 4 levels of personal information identifiability
Grading Grading basis
c) Form an evaluation team, including personal information protection compliance
experts, de-identification technical experts, and relevant business experts.
d) Carry out preliminary research, including detailed research on the data usage
environment.
e) Determine the evaluation basis, including relevant laws, regulations and standards.
f) Determine the re-identification risk calculation scheme and acceptable risk
threshold:
1) The re-identification risk calculation scheme considers both the dataset and the
context in which it is used. It can be based on K anonymous model or
differential privacy model, etc.
2) The acceptable risk threshold meets the corresponding safety requirements and
meets the application needs.
g) Develop an evaluation plan.
6.2 Qualitative evaluation
Qualitative evaluation includes:
a) Identify the identifier according to 5.3 in GB/T 37964-2019. Form a list of
identifiers (including direct identifiers and quasi-identifiers).
b) Determine whether the dataset contains identifiers in the identifier list. If it does
not contain any identifiers, it is rated as level 4 and the evaluation ends; otherwise
continue.
c) Determine whether the dataset has eliminated direct identifiers from the identifier
list. If it contains the direct identifiers in the list, it is rated as level 1, and the
evaluation ends; otherwise, further quantitative evaluation is carried out.
6.3 Quantitative evaluation
Quantitative evaluation includes:
a) Quantitatively calculate the re-identification risk. Carry out re-identification risk
calculation according to the re-identification risk calculation scheme determined
in 6.1f).
b) Compare the calculated re-identification risk results with acceptable risk
thresholds. If the re-identification risk result is less than the acceptable risk
threshold, it is rated as level 3; otherwise, it is rated as level 2, and the evaluation
ends.
See Annex D for the re-identification risk calculation scheme and evaluation example
based on the K-anonymity model.
6.4 Formation of evaluation conclusions
The formation of evaluation conclusions includes:
a) Combining the results of qualitative and quantitative evaluations, a grading
conclusion for de-identification effectiveness is formed.
b) The conclusion is approved by management officials.
6.5 Communication and negotiation
During the evaluation process, maintain communication with relevant parties
(including data providers, data receivers, etc.) and record the communication content,
including:
a) Confirmation of understanding of data sharing purpose and data sharing
environment;
b) Establishment of notification mechanism for major data environment changes;
c) Mutual exchange of information and views on re-identification risk metrics;
d) Opinions expressed by interested parties on the risk of re-identification;
e) Plan for regular/irregular reassessment.
6.6 Evaluation process documentation management
Evaluation process documentation management includes the following.
a) Evaluation process documents include the basis, reference and generated process
documents and result documents during the evaluation process, including but not
limited to:
1) Evaluation plan: including the data set to be evaluated, the environment for
data use, evaluators, evaluation methods, formation of evaluation results and
implementation progress, etc.;
2) Identifier identification report: the process and results of identifier
identification;
3) Re-identification risk calculation scheme: the re-identification risk calculation
scheme and the determination process and results of the acceptable threshold
Annex A
(informative)
Examples for direct identifiers
Any identification number, characteristic or code that uniquely identifies an individual
in a particular context is a direct identifier. Common direct identifiers include, but are
not limited to:
a) Name;
b) Citizenship number;
c) Passport number;
d) Driver's license number;
e) Detailed residential address;
f) Email address;
g) Telephone numbers (including mobile phone numbers and landline numbers);
h) Fax number;
i) Bank account;
j) Vehicle identifier and serial number (including license plate number);
k) Social security number;
l) Health card number;
m) Medical record number;
n) Device identifier and serial number;
o) Biometric identification codes (including identification codes such as fingerprints
and voiceprints);
p) Full face image and any other comparable images;
q) Account number, certificate number or license number;
r) Internet Protocol (IP) addresses.
Annex C
(informative)
Identification of quasi-identifier
C.1 Considerations for identifying quasi-identifiers
Quasi-identifiers are attributes in microdata. Combining with other attributes, it can
uniquely identify the subject of personal information. Usually, the information in the
quasi-identifier can be known by the acquaintances of the personal information subject
or exist in some kind of database.
There are usually some simpler ways of doing things to identify quasi-identifiers. For
example, all other attributes except direct identifiers are regarded as quasi-identifiers.
This method does not consider the possibility of attributes being combined by data
receivers and other background knowledge (other external data sources) for association
attacks. A plethora of quasi-identifiers may be formed. If the K-anonymity method is
used for processing, a large amount of information may be lost, and the de-identified
data cannot support the original application purpose. Another approach is to consider
the possibility of correlation attacks in a more limited way. For example, only attributes
that appear in public datasets are used as quasi-identifiers. This method may cause a
high risk of re-identifying the subject of personal information because of insufficient
judgment on the additional background knowledge that the data recipient or attacker
may have. Therefore, the process of identifying quasi-identifiers needs to consider both
the characteristics of the data itself and the environment in which the data is used
(application purpose, recipients, background knowledge, etc.).
C.2 Methods for identifying quasi-identifiers
The process of quasi-identifier identification begins after direct identifier identification.
First, conduct preliminary identification on the characteristics of the data itself. Then
analyze the environmental factors of data usage. Further screen the final quasi-
identifiers.
a) Rapid identification of quasi-identifiers using prior knowledge: Candidate quasi-
identifiers are quickly identified by comparing with recognized common quasi-
identifiers. See Annex B for examples of common quasi-identifiers.
b) Further identification of quasi-identifiers via attribute correlation: Among the
attributes of the target data set, the attributes with higher correlation are identified.
For example, in the birth registration information database, the baby's date of birth
and discharge date are highly correlated, and the date of birth is recognized as a
common quasi-identifier, so the discharge date highly correlated with it is also
usually identified as a quasi-identifier. Another example: There is also a high
correlation between medication and disease diagnosis, if any one of the attributes
is identified as a quasi-identifier, the other is usually also identified as a quasi-
identifier.
c) Screening of quasi-identifiers based on re-identification risk: The risk of re-
identification of attribute values can be used to further screen quasi-identifiers.
For each attribute, the uniqueness of its value can be calculated. The attribute with
high uniqueness has a higher risk of re-identification. It is also possible to
consider the impact of whether attributes are used as quasi-identifiers on the
number of equivalence classes of the overall data set. For attributes that have a
greater impact, for example, after being used as a quasi-identifier, the number of
equivalence classes increases to a large extent relative to its inaction as a quasi-
identifier, then the attribute needs to be considered as a quasi-identifier.
d) Screening of quasi-identifiers based on environmental risk: When determining the
impact of environmental risks on the identification of quasi-identifiers, it is
necessary to analyze from the perspective of the status and ability to obtain more
background knowledge (background data) and the ability of data recipients to
understand and analyze data.
1) Enterprises or institutions with more personal information, such as insurance
companies (personal medical insurance), hospitals, e-commerce platforms,
etc., usually have strong personal data acquisition capabilities. Therefore, the
probability that such institutions use background knowledge for association re-
identification is usually set as "high". For pharmaceutical or medical device
companies, the background information of personal information they obtain
may be very limited, so the possibility of re-identification can be set to
"medium" or "low" (depending on the specific case requirements).
2) Data recipients with strong data understanding ability and analysis and
processing ability have a higher risk of re-identification. On the contrary, if the
knowledge and ability required for re-identification by using it exceed the
knowledge and ability of the data receiver, the risk of re-identification is low.
3) Through the evaluation of environmental risks, attributes with low probability
of re-identification using background information are usually not identified as
quasi-identifiers. Those with high probability are usually identified as quasi-
identifiers.
......
Source: Above contents are excerpted from the PDF -- translated/reviewed by: www.chinesestandard.net / Wayne Zheng et al.
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