# An Automated Data Desensitisation System Based on the Middle Platform

Authors: Lei Xu; Haocheng Zhou
Journal: Security and Communication Networks
 (2022)

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## Abstract

Built on top of a big data platform, the Middle Platform develops data through abstraction, sharing, and reuse capabilities to provide data products and data services for upper-level business development. While fully analysing and mining the intrinsic value of data, privacy and sensitive information in the data must also be protected, so the Middle Platform needs a data desensitisation system to ensure the safe and open use of data. In order to solve the problems of high usage costs, low efficiency, and lack of standardised results of desensitisation that exist in conventional data desensitisation systems, an automated desensitisation system with data assets, access control, and desensitisation strategies as the main modules is established using an adaptive method of generating dynamic desensitisation rules, combined with a security monitoring mechanism of sensitivity classification and two-level permissions. The system optimises the configuration structure to obtain stable and reliable desensitisation results and efficiently respond to diverse business needs. Users are able to get rid of complex rule management and focus on the data usage itself.


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## Body

## 1. Introduction

The Middle Platform is a data service product featuring data aggregation and governance of cross-domain data (data from different data sources), which drives business development with data development and, thus, improves overall development efficiency. When large amounts of data are deposited in the Middle Platform, accessed, queried, processed, and calculated with high frequency, the risk of compromising user privacy or trade secrets contained in the data grows at a high rate. As a result, international regulations in various industries require data to be privacy-protected before they can be made available for use [1].In current privacy protection practices, data desensitisation is a common technical means of dealing with high efficiency. The original sensitive data is transformed into less sensitive desensitised data by applying a series of data distortions to the more sensitive raw data. Although the desensitised data is distorted to a certain extent, it still has some data value and this distortion is acceptable when balanced against data security and usability. In data desensitisation technology, a desensitisation algorithm is a method of data distortion used in the desensitisation process, which is applied to specific sensitive data to form a desensitisation rule. Desensitisation rules are named after sensitive data and there can be multiple desensitisation rules for one type of sensitive data.The data desensitisation system in the Middle Platform needs to develop a set of suitable desensitisation rules for a sensitive data set according to the user’s business requirements and convert the sensitive data into desensitised data for output. Conventional desensitisation systems have a number of built-in desensitisation rules for each sensitive data item, and the desensitisation task is performed by manually configuring the desensitisation rules for each desensitised data item. This system is essentially management of rules, making it necessary for the user to learn the various desensitisation algorithms and rules before the rules can be configured. Not only is a lot of learning and operational costs invested, but the more sensitive the data and the more complex the business requirements, the more significant the reduction in efficiency; the degree of manual influence is also too deep, and the output desensitised data are not standardised enough to serve the business requirements consistently and even less able to cope with the dynamic desensitisation requirements with high real-time requirements.To solve the abovementioned problems, this study discusses the desensitisation system in a comprehensive manner from multiple perspectives of data management, desensitisation rules, and application scenarios with the background of scenarios and data in the power industry, designs a desensitisation strategy based on adaptive theory, and manages the system user roles with a two-tier mechanism combining sensitive permissions and business requirements. An automated desensitisation system consisting of data assets, access control, and desensitisation strategies as the main modules, with the generation of dynamic desensitisation rules as the core, has been established. The system enables rapid batch desensitisation by configuring desensitisation strategies while taking into account diverse desensitisation needs. The desensitisation results are stable and reliable, and more desensitisation rules can be evolved by adding desensitisation algorithms and desensitisation strategies, making it easier to expand business requirements.


## 2. Materials and Methods

The automated desensitisation system based on the Middle Platform is shown in Figure1.Figure 1
Middle Platform desensitisation system architecture.On the basis of the data assets, the desensitisation system then obtains a collection of sensitive data based on access control, sets the conditions and forms of data opening, and formulates a desensitisation strategy according to the abstracted business requirements. Ultimately, the desensitisation strategy matches the security and availability requirements with the desensitisation strength and algorithm weights, respectively, generating dynamic desensitisation rules to perform the desensitisation task.


### 2.1. Data Assets

The Middle Platform aggregates data from multiple data sources, sorts out the various data structures, and plans the value of the data from a business perspective so that the data forms data assets. For desensitised systems, the management of data assets mainly includes sensitive data identification, sensitive data classification, and sensitive data classification.


#### 2.1.1. Identifying Sensitive Data

Sensitive data usually have a specific or agreed encoding format and rules, and the system can use matching algorithms such as regular expressions and keywords to capture the characteristic fields and obtain sensitive data sets. According to the standards related to the protection of sensitive information in the power industry [2]. Table 1 lists some common sensitive data and their encoding characteristics.Table 1
Encoding structures and rules of common electric power sensitive data.
Sensitive data itemEncoding structure and ruleExampleClient’s no.10-digit serial number0257349261ID card18 digits, 6-digit area code + 8-digit birth date + 4-digit serial number330101197701014237Bank card no.13–19 digits, issuing bank number + card type number + serial number9558801202106562334Electricity addressCity + district/county + street/town + community/village + road + house number16th floor, no. 56, Huaqiao road, Gulou district, NanjingMobile no.11 digits, 3-digit network identifier + 4-digit area code + 4-digit serial number13088886666Electricity consumptionRandom number250, 374, 499Settlement date8 digits, 4-digit “year” + 2-digit “month” + 2-digit “day”20210101


#### 2.1.2. Classifying Sensitive Data

The classification can be based on the source, content and use of the data, etc. and is usually set by the business unit. In this system, the data classification determines the type of business of the data user and is related to the user role setting of the system. Common sensitive data in the power industry is divided into three broad categories [3]: production data, marketing data, and management data. The data listed in Table 1 belong to production data and is mostly used for business services such as development, testing, querying, and sharing; marketing data and management data are mostly used for querying business services.


#### 2.1.3. Grade for Sensitivity

The classification is based on the impact of a breach of the security attributes of the data and is usually set by the business unit. Common sensitive data in the power industry is classified into four levels of confidentiality, with 1 to 4 being progressively more sensitive [4].In this system, there are two classifications of sensitive data throughout its life cycle.Definition 1.
The classification of the sensitivity of the original data are called the original confidentiality level. It is denoted byLO.Definition 2.
The sensitivity level of desensitised data are called the desensitised confidentiality level. It is denoted byLF.
The sensitive data in Table1 are divided into their original classification to obtain Table 2.
After the raw data have been deformed, the desensitised data are less sensitive and more secure. The change in sensitivity from raw to desensitised data depends mainly on the desensitisation intensity [5]. The desensitisation intensity is defined as 3 to 1 from strong to weak according to Table 3, respectively.Table 2
Original confidentiality levels of common electric power sensitive data.
Sensitive data itemOriginal confidentiality levelLOClient’s no.1ID card4Bank card no.4Electricity address3Mobile no.3Electricity consumption2Settlement date1Table 3
Relationship betweenLO, LF, and desensitisation intensity.
LOLFDesensitisation intensity413High22Medium31Low312Medium21Low211Low110No need for desensitisation


### 2.2. Access Control

Access control refers to the configuration of “roles” and their attributes for users of the desensitisation system from a two-dimensional perspective of business applications and user confidentiality, defining who is to be desensitised and what is to be desensitised. The “role” describes the user’s requirements for security and availability of the desensitised results.Definition 3.
The level of sensitivity at which a system allows a user to use data are called sensitive permission. It is denoted byLA, and expresses the security requirements. Again there are 4 levels, with progressively higher permissions from 1 to 4.
For each type of sensitive data, the system must perform desensitisation when the user’sLA<LO and need not perform desensitisation when the user’s LA≥LO.Definition 4.
The matrix of data attributes that the system abstracts and quantitatively assigns to business applications is called business permissions. It is denoted byRA, and expresses usability requirements. It consists of three data attributes: integrity, reality, and repeatability [6]. Assigning a value of 0 or 1 to each attribute yields Table 4.
The main data openness scenarios are development, testing, query, and sharing operations [7]. Therefore, four roles are set up in this system. Taking the use of production data as an example, the specific configuration items are shown in Table 5.Table 4
Description of desensitisation results availability requirements.
AttributeAlgorithm description10IntegrialityWhether to keep the encoding structure intactYNRealityWhether to reflect data real semanticsYNRepeatabilityAre data distortion parameters controllable?RandomQuantitativeKeylessKeyedTable 5
Desensitisation system role configuration.
RoleBusiness scopeSensitive permissionLABusiness permissionsRAIntegrialityRealityRepeatabilityDeveloperDevelopment310, 11TesterTest2110, 1AdministratorEnquiry4———OperatorShare1100—LA≥LO, there is no need for desensitisation.


### 2.3. Desensitisation Strategy

Desensitisation algorithms are data deformation methods used in the desensitisation process, and the application of desensitisation algorithms to specific sensitive data results in desensitisation rules [8]. The system introduces a desensitisation strategy that establishes a link between the data usage requirements of the user’s business and the library of desensitisation algorithms. Using an adaptive strategy model, the desensitisation strategy is designed using the desensitisation strength and the desensitisation algorithm (weight) as factors, while breaking the limits of fixed desensitisation rules, and using the method of generating dynamic desensitisation rules (Figure 2) to perform data desensitisation tasks in various application scenarios.Figure 2
Dynamic rule model for desensitisation system.We consider a sensitive datasetD=D1,D2…Dk, containing k kinds of sensitive data; then,The original confidentiality level of the dataLO=LO1,LO2…LOk;The desensitised confidentiality level of the dataLF=LF1,LF2…LFk;The sensitive permission of the userLA=LA1,LA2…LAk.


#### 2.3.1. Determining the Desensitisation Intensity Range

The user’s sensitivity level must be higher than the final desensitisation level of the data in order for the data to be secure for open use. That is,LAi≥LFi;Via desensitisation, intensityIi=LOi−LFi, i=1,2…k.Then,Ii≥LOi−LAi, and Ii takes a value among 0, 1, 2, and 3.


#### 2.3.2. Assigning Desensitisation Intensity and Desensitisation Algorithms

When the abstract expression of the user’s business requirement isRA, the system matches the desensitisation strategy as DS.The algorithm weightWi for each desensitised data is obtained from the strategy analysis and combined with the intensity range obtained in the previous step; the system selects the final algorithm Ai and intensity Ii in the desensitisation algorithm library and desensitisation intensity grading table.


#### 2.3.3. Generating Dynamic Desensitisation Rules

At the strength ofIi, the data deformation is performed with an algorithm Ai, which constitutes a desensitisation rule Ri for sensitive data i, then there is a desensitisation rule set R=R1,R2…Rk for the entire set of sensitive data.


## 2.1. Data Assets

The Middle Platform aggregates data from multiple data sources, sorts out the various data structures, and plans the value of the data from a business perspective so that the data forms data assets. For desensitised systems, the management of data assets mainly includes sensitive data identification, sensitive data classification, and sensitive data classification.


### 2.1.1. Identifying Sensitive Data

Sensitive data usually have a specific or agreed encoding format and rules, and the system can use matching algorithms such as regular expressions and keywords to capture the characteristic fields and obtain sensitive data sets. According to the standards related to the protection of sensitive information in the power industry [2]. Table 1 lists some common sensitive data and their encoding characteristics.Table 1
Encoding structures and rules of common electric power sensitive data.
Sensitive data itemEncoding structure and ruleExampleClient’s no.10-digit serial number0257349261ID card18 digits, 6-digit area code + 8-digit birth date + 4-digit serial number330101197701014237Bank card no.13–19 digits, issuing bank number + card type number + serial number9558801202106562334Electricity addressCity + district/county + street/town + community/village + road + house number16th floor, no. 56, Huaqiao road, Gulou district, NanjingMobile no.11 digits, 3-digit network identifier + 4-digit area code + 4-digit serial number13088886666Electricity consumptionRandom number250, 374, 499Settlement date8 digits, 4-digit “year” + 2-digit “month” + 2-digit “day”20210101


### 2.1.2. Classifying Sensitive Data

The classification can be based on the source, content and use of the data, etc. and is usually set by the business unit. In this system, the data classification determines the type of business of the data user and is related to the user role setting of the system. Common sensitive data in the power industry is divided into three broad categories [3]: production data, marketing data, and management data. The data listed in Table 1 belong to production data and is mostly used for business services such as development, testing, querying, and sharing; marketing data and management data are mostly used for querying business services.


### 2.1.3. Grade for Sensitivity

The classification is based on the impact of a breach of the security attributes of the data and is usually set by the business unit. Common sensitive data in the power industry is classified into four levels of confidentiality, with 1 to 4 being progressively more sensitive [4].In this system, there are two classifications of sensitive data throughout its life cycle.Definition 1.
The classification of the sensitivity of the original data are called the original confidentiality level. It is denoted byLO.Definition 2.
The sensitivity level of desensitised data are called the desensitised confidentiality level. It is denoted byLF.
The sensitive data in Table1 are divided into their original classification to obtain Table 2.
After the raw data have been deformed, the desensitised data are less sensitive and more secure. The change in sensitivity from raw to desensitised data depends mainly on the desensitisation intensity [5]. The desensitisation intensity is defined as 3 to 1 from strong to weak according to Table 3, respectively.Table 2
Original confidentiality levels of common electric power sensitive data.
Sensitive data itemOriginal confidentiality levelLOClient’s no.1ID card4Bank card no.4Electricity address3Mobile no.3Electricity consumption2Settlement date1Table 3
Relationship betweenLO, LF, and desensitisation intensity.
LOLFDesensitisation intensity413High22Medium31Low312Medium21Low211Low110No need for desensitisation


## 2.1.1. Identifying Sensitive Data

Sensitive data usually have a specific or agreed encoding format and rules, and the system can use matching algorithms such as regular expressions and keywords to capture the characteristic fields and obtain sensitive data sets. According to the standards related to the protection of sensitive information in the power industry [2]. Table 1 lists some common sensitive data and their encoding characteristics.Table 1
Encoding structures and rules of common electric power sensitive data.
Sensitive data itemEncoding structure and ruleExampleClient’s no.10-digit serial number0257349261ID card18 digits, 6-digit area code + 8-digit birth date + 4-digit serial number330101197701014237Bank card no.13–19 digits, issuing bank number + card type number + serial number9558801202106562334Electricity addressCity + district/county + street/town + community/village + road + house number16th floor, no. 56, Huaqiao road, Gulou district, NanjingMobile no.11 digits, 3-digit network identifier + 4-digit area code + 4-digit serial number13088886666Electricity consumptionRandom number250, 374, 499Settlement date8 digits, 4-digit “year” + 2-digit “month” + 2-digit “day”20210101


## 2.1.2. Classifying Sensitive Data

The classification can be based on the source, content and use of the data, etc. and is usually set by the business unit. In this system, the data classification determines the type of business of the data user and is related to the user role setting of the system. Common sensitive data in the power industry is divided into three broad categories [3]: production data, marketing data, and management data. The data listed in Table 1 belong to production data and is mostly used for business services such as development, testing, querying, and sharing; marketing data and management data are mostly used for querying business services.


## 2.1.3. Grade for Sensitivity

The classification is based on the impact of a breach of the security attributes of the data and is usually set by the business unit. Common sensitive data in the power industry is classified into four levels of confidentiality, with 1 to 4 being progressively more sensitive [4].In this system, there are two classifications of sensitive data throughout its life cycle.Definition 1.
The classification of the sensitivity of the original data are called the original confidentiality level. It is denoted byLO.Definition 2.
The sensitivity level of desensitised data are called the desensitised confidentiality level. It is denoted byLF.
The sensitive data in Table1 are divided into their original classification to obtain Table 2.
After the raw data have been deformed, the desensitised data are less sensitive and more secure. The change in sensitivity from raw to desensitised data depends mainly on the desensitisation intensity [5]. The desensitisation intensity is defined as 3 to 1 from strong to weak according to Table 3, respectively.Table 2
Original confidentiality levels of common electric power sensitive data.
Sensitive data itemOriginal confidentiality levelLOClient’s no.1ID card4Bank card no.4Electricity address3Mobile no.3Electricity consumption2Settlement date1Table 3
Relationship betweenLO, LF, and desensitisation intensity.
LOLFDesensitisation intensity413High22Medium31Low312Medium21Low211Low110No need for desensitisation


## 2.2. Access Control

Access control refers to the configuration of “roles” and their attributes for users of the desensitisation system from a two-dimensional perspective of business applications and user confidentiality, defining who is to be desensitised and what is to be desensitised. The “role” describes the user’s requirements for security and availability of the desensitised results.Definition 3.
The level of sensitivity at which a system allows a user to use data are called sensitive permission. It is denoted byLA, and expresses the security requirements. Again there are 4 levels, with progressively higher permissions from 1 to 4.
For each type of sensitive data, the system must perform desensitisation when the user’sLA<LO and need not perform desensitisation when the user’s LA≥LO.Definition 4.
The matrix of data attributes that the system abstracts and quantitatively assigns to business applications is called business permissions. It is denoted byRA, and expresses usability requirements. It consists of three data attributes: integrity, reality, and repeatability [6]. Assigning a value of 0 or 1 to each attribute yields Table 4.
The main data openness scenarios are development, testing, query, and sharing operations [7]. Therefore, four roles are set up in this system. Taking the use of production data as an example, the specific configuration items are shown in Table 5.Table 4
Description of desensitisation results availability requirements.
AttributeAlgorithm description10IntegrialityWhether to keep the encoding structure intactYNRealityWhether to reflect data real semanticsYNRepeatabilityAre data distortion parameters controllable?RandomQuantitativeKeylessKeyedTable 5
Desensitisation system role configuration.
RoleBusiness scopeSensitive permissionLABusiness permissionsRAIntegrialityRealityRepeatabilityDeveloperDevelopment310, 11TesterTest2110, 1AdministratorEnquiry4———OperatorShare1100—LA≥LO, there is no need for desensitisation.


## 2.3. Desensitisation Strategy

Desensitisation algorithms are data deformation methods used in the desensitisation process, and the application of desensitisation algorithms to specific sensitive data results in desensitisation rules [8]. The system introduces a desensitisation strategy that establishes a link between the data usage requirements of the user’s business and the library of desensitisation algorithms. Using an adaptive strategy model, the desensitisation strategy is designed using the desensitisation strength and the desensitisation algorithm (weight) as factors, while breaking the limits of fixed desensitisation rules, and using the method of generating dynamic desensitisation rules (Figure 2) to perform data desensitisation tasks in various application scenarios.Figure 2
Dynamic rule model for desensitisation system.We consider a sensitive datasetD=D1,D2…Dk, containing k kinds of sensitive data; then,The original confidentiality level of the dataLO=LO1,LO2…LOk;The desensitised confidentiality level of the dataLF=LF1,LF2…LFk;The sensitive permission of the userLA=LA1,LA2…LAk.


### 2.3.1. Determining the Desensitisation Intensity Range

The user’s sensitivity level must be higher than the final desensitisation level of the data in order for the data to be secure for open use. That is,LAi≥LFi;Via desensitisation, intensityIi=LOi−LFi, i=1,2…k.Then,Ii≥LOi−LAi, and Ii takes a value among 0, 1, 2, and 3.


### 2.3.2. Assigning Desensitisation Intensity and Desensitisation Algorithms

When the abstract expression of the user’s business requirement isRA, the system matches the desensitisation strategy as DS.The algorithm weightWi for each desensitised data is obtained from the strategy analysis and combined with the intensity range obtained in the previous step; the system selects the final algorithm Ai and intensity Ii in the desensitisation algorithm library and desensitisation intensity grading table.


### 2.3.3. Generating Dynamic Desensitisation Rules

At the strength ofIi, the data deformation is performed with an algorithm Ai, which constitutes a desensitisation rule Ri for sensitive data i, then there is a desensitisation rule set R=R1,R2…Rk for the entire set of sensitive data.


## 2.3.1. Determining the Desensitisation Intensity Range

The user’s sensitivity level must be higher than the final desensitisation level of the data in order for the data to be secure for open use. That is,LAi≥LFi;Via desensitisation, intensityIi=LOi−LFi, i=1,2…k.Then,Ii≥LOi−LAi, and Ii takes a value among 0, 1, 2, and 3.


## 2.3.2. Assigning Desensitisation Intensity and Desensitisation Algorithms

When the abstract expression of the user’s business requirement isRA, the system matches the desensitisation strategy as DS.The algorithm weightWi for each desensitised data is obtained from the strategy analysis and combined with the intensity range obtained in the previous step; the system selects the final algorithm Ai and intensity Ii in the desensitisation algorithm library and desensitisation intensity grading table.


## 2.3.3. Generating Dynamic Desensitisation Rules

At the strength ofIi, the data deformation is performed with an algorithm Ai, which constitutes a desensitisation rule Ri for sensitive data i, then there is a desensitisation rule set R=R1,R2…Rk for the entire set of sensitive data.


## 3. Results

It is assumed that the application data are tested on the Middle Platform of STATE GRID JIANGSU ELECTRIC POWER COMPANY data for the bill payment function.The system performs desensitisation tasks according to the flow in Figure3.Figure 3
Automated data desensitisation flow chart.The user’s role for this data use application is “Tester.”A production data set is extracted from the Middle Platform, and the data to be desensitised includes: customer number, telephone number, bank card number, electricity consumption address, electricity consumption, and billing date. The abovementioned sensitive information can be identified using regular expressions and keyword algorithms to obtain the sensitive data set. Combining Tables1 and 2, Table 6 shows the encoding rules and the original confidentiality level LO for these sensitive data.Table 6
Encoding structure and original classification of sensitive data.
No.Sensitive data itemEncoding structure and ruleExampleLO1Client’s no10-digit serial number025734926112Mobile no.11 digits, 3-digit network identifier + 4-digit area code + 4-digit serial number1308888666633Bank card no.13–19 digits, issuing bank number + card type number + serial number955880120210656233444Electricity addressCity + district/county + street/town + community/village + road + house number16th floor, no. 56, Huaqiao road, Gulou district, Nanjing35Electricity consumptionRandom number250, 374, 49926Settlement date8 digits, 4-digit “year” + 2-digit “month” + 2-digit “day”202101011According to Table5, the “Tester’s” sensitive permission LA=2 and business permissions of the test application RA =  1,1,1 or 1,0,1.Then the desensitisation intensity setI=I1,I2,I3,I4,I5,I6 for the 6 sensitive data, Ii takes the values in Table 7.Table 7
Desensitisation algorithms.
No.Sensitive data itemDesensitisation intensity values1Client’s no02Mobile no.1, 23Bank card no.1, 2, 34Electricity address1, 25Electricity consumption06Settlement date0A library of desensitisation algorithms is available in the system, as shown in Table8 [9].Table 8
Desensitisation algorithms and weights.
AlgorithmDescriptionExampleWeightMaskUse symbol “∗” to replace parts of the data, with the data length unchanged.13088886666 -> 130∗∗∗∗∗∗∗∗1, 0, 1FloorTake an integer—0, 0, 0∗HashingMap data into a fixed-length string13088886666 -> abcdef0, 0, 1TruncationCut parts of the data13088886666 -> 1300, 0, 1ShiftAdd an constant offset13088886666 -> 130888866701, 1, 0SynthesisSimulate new data to replace the original data13088886666 -> 130110077881, 1, 1RearrangeSort a column of values upside-down—0, 0, 0∗The various sensitive data in Table6 were graded for desensitisation intensity to get Table 9 [10].Table 9
Sensitive data desensitisation intensity grading.
No.Sensitive data itemLODesensitisation intensity gradeReserved bitDesensitised bitsExample1Client’s no10Keep all the 1000257886496 -> 02578864962Mobile no.32Keep the top 3The bottom 813088886666 -> 130∗∗∗∗∗∗∗∗1Keep the top 3 & bottom 4The middle 413088886666 -> 130∗∗∗∗66663Bank card no.43Keep the top 4The bottom 159558801202106562334 -> 9558∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗2Keep the top 4 & bottom 4The middle 119558801202106562334 -> 9558∗∗∗∗∗∗∗∗∗∗∗∗23341Keep the top 8 & bottom 4The middle 79558801202106562334 -> 95588012∗∗∗∗∗∗∗23344Electricity address32Keep “city” & “district/county” & “house no.”Street/town + community/village + road16th floor, no. 56, Huaqiao road, Gulou district, Nanjing -> 16th floor, no. 56,∗∗∗ road, Gulou district, ∗∗∗1Keep “street/town” & “community/village” & “road”City + district/county + house no.16th floor, no. 56, Huaqiao road, Gulou district, Nanjing ->∗∗∗, ∗∗∗, Huaqiao road, Gulou district, Nanjing5Electricity consumption211116Settlement date10Keep all the 8020210108 -> 202101081For random numerical type sensitive data, because there was no coding structure limit, the deformation effects were mainly affected by the algorithm. So, the default desensitisation intensity was equal to the configured value.The desensitisation strategies available in the system are shown in Table10.Table 10
Desensitisation strategies of the system.
Strategy nameDescriptionIntensityAlgorithm weightingMaximum strengthTake the highest value of desensitisation intensityMax {Ii}RASame algorithmConsistent algorithms for all sensitive dataIiconfiguration item, such as “mask”Minimum enoughTake the lowest value of desensitisation intensityMin {Ii}RABest simulation effectData emulation as far as possible on a confidential level permitIiRAIf the user specifies the strategy “minimum enough,” the desensitisation strengths and desensitisation algorithms for the 6 sensitive data types can be obtained by filtering from the strength classification Table8 and the algorithm weight vector Table 9, and the composed desensitisation rules and final desensitisation results are shown in Table 11.Table 11
Desensitisation rules and results under “minimum enough.”
RuleIntensityDesensitised bitsAlgorithmResultClient’s no.0——0257349261 -> 0257349261Mobile no.1The middle 4Mask13088886666 -> 130∗∗∗∗6666Bank card no.1The middle 7Synthesis9558801202106562334 -> 9558801223645452334Electricity address1City + district/county + house no.Synthesis16th floor, no. 56, Huaqiao road, Gulou district, Nanjing -> 1th floor, no. 1 Huaqiao road, Gulou district, NanjingElectricity consumption0——250, 374, 499 -> 250, 374, 499Settlement date0——20210101 -> 20210101


## 4. Discussion

The desensitisation system differs from traditional systems in, that it, transforms the management of desensitisation rules into the management of desensitisation strategies, making the desensitisation strategies more strongly coupled with the business applications, and freeing the desensitisation rules to focus on data deformation processing. The sensitivity classification of data and the abstraction of application scenarios are used as a means to quantitatively assess the security and availability requirements of business applications for desensitisation results, enabling the system to achieve standardised and automated desensitisation. The criteria for sensitivity classification and application scenario abstraction are set by the business unit and may be dynamically adjusted as the data changes in terms of aggregation, volume, and scale, and as business needs expand in terms of diversity and timeliness. This is a future research direction for this system. However, as both the desensitisation intensity table and the algorithm weighting table are only relevant to the data deformation process itself, they are independent of the grading scale and business abstraction methods, so they will not be affected by changes in the standards set by the business sector, and the system remains well suited to the open use of sensitive data in the Middle Platform.



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