Privacy Management of Chinese Youth in the Age of Algorithms

Yibing Tang

School of Public Administration and Media, Beijing Information Science and Technology University, Beijing, China

Keywords: Algorithm, Chinese Youth Group, Privacy Management, The Theory of Planned Behavior.

Abstract: In the age of algorithm, people can receive the information they are interested in more and more quickly.

Although algorithm recommendation brings convenience to people and reduces time cost, some platforms

collect users' personal information without the users realizing it. The purpose of these platforms to collect

user information is to more directly and accurately understand the user's content preferences. However, this

approach brings a great threat to users' privacy and security. In this case, people pay more and more attention

to privacy management. This paper is based on the above background and the Chinese youth’s privacy

management in the age of algorithms. The author conducted a quantitative study through a questionnaire

survey combining online and offline form in order to explore the influencing factors of privacy protection

attitude of Chinese youth groups. This study takes the Theory of Planned Behavior as the core and mainly in

discuss the attitude towards the behavior. The final conclusion is as follows: demographic variables and

privacy ownership affect the behavior beliefs; demographic variables, the knowledge of the result of privacy

invasion and no inconvenience has been caused by privacy management influence the outcome evaluations

together. Because behavior beliefs and outcome evaluations are two factors that affect the attitude towards

the behavior, the author conclude that privacy ownership, demographic variables, the knowledge of the result

of privacy invasion and no inconvenience has been caused by privacy management influence the attitude

together.

1 INTRODUCTION

Recommendation Algorithm is a double-edged sword,

it generally brings convenience for people, in the

meantime it makes many people fall into an

ambivalence which means that it will bring some

privacy problems. At this point, some people started

to protect their privacy by some defensive ways with

the purpose of “gaming the algos” or “confuse

algorithms” (Head, 2020), such as, giving the social

media some wrong answer or creating more than one

accounts on the same platform.

Many studies have been discussed how algorithm

impact people’s daily life and their reading behavior

from the perspective of elite and technical

determinism. The author focuses on the privacy

management in people's lives, and tries to figure out

how they deal with these privacy issues.

This article mainly discusses the attitude and

behavior towards algorithm privacy acquisition from

Chinese youth group and want to show the readers

how the new Chinese youth group fight against

algorithm and the “tipping point” (Gladwell, 2006) for

protecting their privacy by the defensive way. The

research method used in this article is questionnaire

survey and the questions based on the Theory of

Planned Behavior. Also, this study depicted the whole

picture of the new generation how they treat the

privacy acquisition in the age of algorithms and why

they protect their privacy by some defensive ways.

2 DEFINITION OF RELATED

CONCEPTS

2.1 “Youth”

There are four types of division criteria in the

definition of age including calendar age, physiological

age, psychological age and social age (Li, 2009),

which are respectively applicable to different fields

and situations.

In 1968, the United Nations Educational,

Scientific and Cultural Organization made three

definitions of youth in its report on youth. The first

point is the age group between 15 and 24 years old,

152

Tang, Y.

Privacy Management of Chinese Youth in the Age of Algorithms.

DOI: 10.5220/0011732000003607

In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages 152-162

ISBN: 978-989-758-620-0

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

the second point is focus on the special educational,

social and family situations, those who did not start

school, did not start work or set up a family were

defined as youth. Third, youth is defined by their state

of mind. They are imaginative, courageous rather than

cowardly, adventurous rather than pleasure-seeking

(Uneso, 1968). Social age is not mentioned in this

report, which is an age standard based on the maturity

of social behavior.

In the social environment of China, physiological

age and psychological age more used for academic

research. And Social age is more used for the

formulation of laws and regulations. Therefore, the

calendar age which defines the lower limit and upper

limit of the age of youth is easier to distinguish and

common in use.

Considering that the sample of this study is

Chinese youth, the author decided to adopt the most

common definition of youth age range in China-15

~34 years old for the calendar age.

2.2 Personal Privacy

Warren et al. put forward the concept of personal

privacy for the first time and published in 1890

(Warren, 1890). Personal privacy is a unique right that

should be protected. In many years of academic

research on privacy, the definition of privacy is mainly

divided into two categories: one is based on value and

regards privacy as a commodity (Bennett, 1995); the

other is based on homology and regards privacy as a

state (including anonymity, concealment, reservation

and secrecy) or a control (Westin, 1968).

2.3 Algorithm and Privacy Disclosure

There are most platform will allow users to set some

permissions, such as microphone, location and so

forth, before using the algorithm platform. Therefore,

in this case, the legitimate privacy acquisition

confirmed by users is recognized, which does not

belong to the definition of privacy disclosure in this

study; while obtaining privacy without giving a

prompt belongs to the definition of privacy disclosure

in this study. Taking algorithms and privacy leaks as

an example, when a conversation is “listened” by a

mobile phone, then there is an advertisement for items

related to the conversation content in the shopping

software.

2.4 Theoretical Framework and

Hypothesis

• Theory of Planned Behavior

American psychologists M. Fishbein and I. Ajzen put

forward the Theory of Reasoned Action, in which

subjective norms and attitudes play a leading role in

the behavioral intention.

Later, Ajzen found that people's behavior is not

entirely voluntary, but under some kind of control.

Therefore, he expanded Theory of Reasoned Action

and added a new concept of "perceived behavior

control" to develop into a new model called Theory of

Planned Behavior (Ajzen, 1991).

According to Theory of Planned Behavior (as

shown in figure 1), behavior intention is the

antecedent of actual usage. Moreover, attitude,

subjective norm and perceived behavioral control are

the three main variables that determine behavioral

intention. The more positive attitude, the greater

support from salient groups, the stronger perceived

behavioral control, the behavioral intention will be

greater. The detailed explanation of the three main

variables of the Theory of Planned Behavior is

following.

Figure 1: Theoretical Framework-Theory of Planned Behavior.

Privacy Management of Chinese Youth in the Age of Algorithms

153

• Attitude toward The Behavior

Attitude is the core concept of the Theory of Planned

Behavior, and the attitude toward behavior is the

evaluation of the degree to which an individual likes

or dislikes to perform a specific behavior. Behavior

attitude includes two parts: one is behavior beliefs, the

other is outcome evaluation. The author infers that

demographic variables and privacy ownership affect

the behavior beliefs; demographic variables, the

knowledge of the result of privacy invasion and no

inconvenience has been caused by privacy

management influence the outcome evaluations

together. Because behavior beliefs and outcome

evaluations are two factors that affect the attitude

towards the behavior. So, the author thinks that

privacy ownership, demographic variables (include

age, gender, education and major), the knowledge of

the result of privacy invasion and no inconvenience

has been caused by privacy management influence the

attitude together. Therefore, the author makes the

following hypothesis:

H1. Demographic variables affect the attitude

toward privacy control.

H2. Privacy ownership affects the attitude toward

privacy control.

H3. The knowledge of the result of privacy

invasion affects the attitude toward privacy control.

H4. No inconvenience has been caused by privacy

management affects the attitude toward privacy

control.

2.4.1 Subject Norm

Subjective norm refers to the social pressure that

individuals feel about whether or not to carry out a

certain behavior. It reflects the influence of salient

individuals or groups on individual behavior decision.

Subjective norms are influenced by normative belief

and motivation to comply. "Normative belief" refers

to an individual's imagination of whether the salient

individuals or groups should perform a specific

behavior; “motivation to comply” is an individual's

intention to comply with the expectation of the salient

individuals or groups (Fishbein, 1977).

2.4.2 Perceived Behavior Control

Perceived behavioral control refers to a hinder from

individual's past experience and expectation, which

reflects an individual's perception of the factors that

promote or hinder executive behavior. When

individuals think that the more resources and

opportunities they have, the less obstacles they expect,

the stronger their perceptual behavior control over

their behavior. There are two ways of influencing

perceptual behavior control: one is that it has

motivational implications for behavior intention; the

other is that it can directly predict behavior.

3 METHODOLOGY

The research objects are the Chinese youth who had

used algorithm platform from 15 to 34 years old. This

study adopts the survey method of combining paper

questionnaire and network questionnaire in order to

obtain the real and effective opinions of the public.

Paper questionnaire is used for pilot study and

semi-structured interview before the formal

questionnaire, the pilot study sample is 20 randomly

selected friends around the author. After collecting the

paper questionnaire, the author interviewed them

about the difficulty of filling in the questionnaire, the

time spent and the suggestions of questionnaire

design. Then, the author uses SPSS to analyze the

reliability and validity of this study. A small number

of items affecting the reliability and validity of

variables were deleted, and finally a formal

questionnaire was formed.

Formal questionnaire survey was conducted

online, which is distributed and collected through

WeChat, Douban, Weibo and other forms by using the

Questionnaire Star online survey platform. It mainly

conducted from March 10, 2021 to March 15, 2021,

and a total of 107 questionnaires were collected. After

screening, a total of 94 valid questionnaires were

obtained, and the qualified rate was 87.9%.

The main screening methods are as follows. First,

the answer from the respondents who are not in the

sample spacing are deleted. Secondly, the filling time

is too short or too long was eliminated. Thirdly, the

author added the verification question in the

questionnaire design (for example,” Please choose the

second option”), and eliminated the questionnaires

with incorrect answers.

3.1 Measure

On the basis of the previous maturity scale and

research content, this study combined with the

situation of Chinese youth to make corresponding

modifications and form a questionnaire. (as shown in

Table 1).

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Table 1: The Variable and Indexes.

Variable Indexes References

Attitude I'm very sensitive when someone or an organization asks me to provide

ersonal information.

(Bansal, 2008)

I'm worry about the privacy leakage in the process of using the Internet.

I'm worry that using algorithmic recommendation will reveal my privacy.

I'm puzzle by I can't control how the software uses my information.

Demographic

variables

Age

Gender

Education

Major

Privacy ownership I always pay attention to the request permission when installing the platform.

I think I need privacy protection.

I pay more attention to privacy protection than most people.

Knowledge of the

result of privacy

invasion

I've experienced personal information being leaked. (Smith, 1996;

Smith, 2011)

I often hear some personal privacy has been leaked from the media reports.

There are relatives or friends who have been divulged of their personal

rivac

, resultin

in the disru

tion of their normal life.

No inconvenience has

been caused by

rivacy management

I've protected my privacy in some defensive ways.

Privacy protection in the platform hasn't caused inconvenience for me.

Table 2: Questionnaire results-"I'm worry about the privacy leakage in the process of using the Internet."

Table 3: Questionnaire results- "I'm worry that using algorithmic recommendation will reveal my privacy."

4 DATA ANALYSIS

4.1 Attitude

There are 96.8% of the research objects worry about

privacy leakage in the process of using the Internet

((as shown in Table 2), while the research objects who

worry about privacy leakage in the process of using

the recommendation algorithm are slightly less than

the former, but still as high as 92.6% ((as shown in

Table 3). And 72.3% of the respondents were worried

that they could not control how the algorithm platform

used their information (as shown in Table 4).

When some individuals or groups try to obtain

their personal information, 92.6% of the research

objects are sensitive to it and worry about their

privacy. Nevertheless, 7.4% of the people don't care

about it (as shown in Table 5).

According to the questionnaire answers of the

attitude part, the vast majority of people are worried

about privacy leakage (including online platform

access and offline access), but a few people are not

aware of the privacy problems that algorithms may

bring.

ate

ercentage

umu

ve percentage

YES 91 96.8 96.8

NO 3 3.2 100.0

Total 94 100.0

Rate Percentage Cumulative percentage

YES 87 92.6 92.6

NO 7 7.4 100.0

Total 94 100.0

o . wo y t at us g a go t c eco e dat o w evea y p vacy.

Privacy Management of Chinese Youth in the Age of Algorithms

155

Table 4: Questionnaire results- "I'm puzzle by I can't control how the software uses my information."

Table 5: Questionnaire results- "I'm very sensitive when someone or an organization asks me to provide personal information."

Table 6: Questionnaire results-Age.

4.2 The Relationship Between Attitude

and Age

The age distribution of all the research object is shown

in Table 6, and the age of the subjects is widely

concentrated in 19-23 years old. In the study of the

relationship between the attitude towards privacy

management behavior and age, the author adopts the

linear regression method. The analysis results show

that in the table ANOVAa (as shown in Table 7), Sig

is 0.192b, which is much higher than 0.05 (in statistics,

SIG < 0.05 is generally considered as significant

coefficient test) In the Model summary (as shown in

Table 8), R is the coefficient of determination. The

result shows that the R of age and attitude is + 0.136a,

so the relationship between them is weak and positive.

In other words, the higher age of the research object,

the more obvious the attitude and attention of the

algorithm platform to obtain privacy information.

4.3 The Relationship Between Attitude

and Gender

The gender distribution of all the respondents is shown

in Table 9, of which 23.4% are male and 76.6% are

female. In the study of the relationship between the

attitude towards privacy management behavior and

gender, the author adopts the method of linear

regression. The analysis results show that in the table

ANOVAa (as shown in Table 10), Significance is

0.740b, which is much higher than 0.05. The result

shows that the correlation coefficient of gender and

attitude is + 0.035a (as shown in Table 11), so there is

almost no relationship between them.

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Table 7: Statistical Data- ANOVAa between attitude and age.

Table 8: Statistical Data-Model Summary between attitude and age.

Table 9: Questionnaire results-Gender.

Table 10: Statistical Data-ANOVAa between attitude and gender.

Table 11: Statistical Data-Model Summary between attitude and gender.

4.4 The Relationship Between Attitude

and Education

The education of all respondents is shown in Table 12,

most of which are undergraduate degrees. In the study

of the relationship between the attitude towards

privacy management behavior and education, the

author adopts the method of linear regression. The

analysis results show that in the ANOVAa (as shown

in Table 13), Sig is 0.210, which is much higher than

0.05. In the model summary, the correlation

coefficient of education and attitude is + 0.131a (as

shown in Table 14), so the relationship between them

is weak and positive. That is to say, the higher

education degree of the research object, the more

obvious the attitude and attention of the algorithm

platform to obtain privacy information.

4.5 The Relationship Between Attitude

and Major

The majors of all the respondents are shown in Table

15, among which management, mathematics and

literature account for the highest proportion. In the

study of the relationship between the attitude towards

privacy management behavior and majors, the author

divides the 13 kinds of majors commonly used in

Chinese universities into two groups base on the level

of logicality, and adopts the linear regression method.

The analysis results show that in ANOVAa (as shown

in Table 16), Sig is 0.62b, which is higher than 0.05.

In the model summary, R of the major and attitude is

+ 0.193a (as shown in Table 17), so the relationship

between them is weak and positive. That is to say, the

higher logicality major they study, the more obvious

the attitude and attention of the algorithm platform to

obtain privacy information.

Model Sum of Squares df Mean Square F Sig.

1 Regression 0.008 1 0.008 0.11 .740b

Residual 6.471 92 0.07

Total 6.479 93

b Predictors: (Constant), Gender

ANOVAa

a Dependent Variable: Attitude

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .035a 0.001 -0.01 0.265

Model Summary

a Predictors: (Constant), Gender

Privacy Management of Chinese Youth in the Age of Algorithms

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Table 12: Questionnaire results-Education.

Table 13: Statistical Data-ANOVAa between attitude and education.

Table 14: Statistical Data-Model Summary between attitude and education.

Table 15: Questionnaire results-Major.

Table 16: Statistical Data-ANOVAa between attitude and major.

Table 17: Statistical Data-Model Summary between attitude and major.

Model Sum of Squares df Mean Square F Sig.

1 Regression 0.11 1 0.11 1.595 .210b

Residual 6.368 92 0.069

Total 6.479 93

b Predictors: (Constant), Education

ANOVAa

a Dependent Variable: Attitude

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .131a 0.017 0.006 0.263

Model Summary

a Predictors: (Constant), Education

Model Sum of Squares df Mean Square F Sig.

1 Regression 0.242 1 0.242 3.572 .062b

Residual 6.237 92 0.068

Total 6.479 93

a Dependent Variable: Attitude

b Predictors: (Constant), Major

ANOVAa

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Table 18: Questionnaire results- "I always pay attention to the request permission when installing the platform."

Table 19: Questionnaire results- "I think I need privacy protection."

Table 20: Questionnaire results- "I pay more attention to privacy protection than most people."

Table 21: Statistical Data-ANOVAa between attitude and privacy ownership.

Table 22: Statistical Data-Model Summary between attitude and privacy ownership.

4.6 Privacy Ownership

According to the survey results of the privacy

ownership, 95.7% of the respondents think they need

privacy protection (as shown in Table 19), and far

more than half of the respondents think they pay more

attention to privacy protection (as shown in Table 20)

than others which means that they agree with their

right of privacy. At the same time, 92.6% of the

subjects had pay attention to the setting of platform

permissions (as shown in Table 18), which means that

the agreement of their right to control privacy.

The above research results show that most people

recognize their privacy ownership when using the

algorithm platform, but a large number of people think

that they pay more attention to privacy protection than

others, which means that they are ambitious about

their privacy management behavior, and their

understanding is not comprehensive enough.

4.7 The Relationship Between Attitude

and Privacy Ownership

In the study of the relationship between the attitude

toward privacy management and privacy ownership,

the author uses linear regression method to analyze.

The results showed that in the analysis of variance (as

shown in Table 21), SIG was 0.00b, lower than 0.05.

In Model Summary, the R value of privacy and

attitude is + 0.370a (as shown in Table 22), and the

relationship between them is positive. In other words,

the more Chinese youth have the right to privacy, the

more obvious the attitude of the algorithm platform to

obtain private information.

Rate Percentage Cumulative percentage

YES 87 92.6 92.6

NO 7 7.4 100.0

Total 94 100.0

Form9.

ypy q p g p

Rate Percentage Cumulative percentage

VERY DISAGREE 0 0.0 0.0

DISAGREE 1 1.1 1.1

NO NECESSARILY 3 3.2 4.3

AGREE 25 26.6 30.9

VERY AGREE 65 69.1 100.0

Total 94 100.0

pyp

Model Sum of Squares df Mean Square F Sig.

1 Regression 0.887 1 0.887 14.597 .000b

Residual 5.592 92 0.061

Total 6.479 93

ANOVAa

a Dependent Variable: Attitude

b Predictors: (Constant), Privacy ownership

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .370a 0.137 0.128 0.247

Model Summary

a Predictors: (Constant), Privacy ownership

Privacy Management of Chinese Youth in the Age of Algorithms

159

4.8 Knowledge of The Result of

Privacy Invasion

This part mainly focuses on the source and

understanding of privacy disclosure. There are more

than 70% of the subjects who have been experienced

the privacy invasion (as shown in Table 23). At the

same time, there are only 39.4% of them heard about

the situation and consequences of privacy leakage

from their relatives or friends (as shown in Table 25).

Combined with the results of Table 23, it can be

inferred that the topic of privacy leakage is rarely

mentioned among relatives or friends. In addition,

93.6% of the subjects often hear or see the news about

personal privacy disclosure in the media (as shown in

Table 24), which brings great convenience to the

dissemination of knowledge about privacy disclosure.

4.9 The Relationship Between Attitude

and Knowledge of The Result of

Privacy Invasion

In the study of the relationship between attitude and

the knowledge of the result of privacy invasion, the

author uses linear regression method to analyze. The

results show that sig is 0.379b (as shown in Table 26),

which is much higher than 0.05b. In the model

summary, the R value of privacy and attitude is +

0.092a (as shown in Table 27), and the relationship

between them is weakly positive. In other words, the

more Chinese youth have knowledge of the result of

privacy invasion, the more obvious their attitude

towards the algorithm platform to obtain private

information.

Table 23: Questionnaire results- "I've experienced personal information being leaked."

Table 24: Questionnaire results- "I often hear some personal privacy has been leaked from the media reports."

Table 25: Questionnaire results- "There are relatives or friends who have been divulged of their personal privacy, resulting in

the disruption of their normal life."

Table 26: Statistical Data-ANOVAa between attitude and Knowledge of the result of privacy invasion.

Table 27: Statistical Data-Model Summary between attitude and Knowledge of the result of privacy invasion.

Rate Percentage Cumulative percentage

YES 66 70.2 70.2

NO 28 29.8 100.0

Total 94 100.0

pp g

Rate Percentage Cumulative percentage

YES 37 39.4 39.4

NO 57 60.6 100.0

Total 94 100.0

Model Sum of Squares df Mean Square F Sig.

1 Regression 0.054 1 0.054 0.78 .379b

Residual 6.424 92 0.07

Total 6.479 93

ANOVAa

a Dependent Variable: Attitude

b Predictors: (Constant), Knowledge of the result of privacy invasion

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .092a 0.008 -0.002 0.264

Model Summary

a Predictors: (Constant), Knowledge of the result of privacy invasion

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4.10 No Inconvenience Has Been Caused

by Privacy Management

In all the questionnaire results, 72.3% people think

that privacy management does not bring the

inconvenience (as shown in Table 29). In the specific

implementation process (as shown in Table 28),

85.1% of the research objects will protect their privacy

in the algorithm platform by turning off the

permission of microphone, location, etc., in addition,

79.8% of the research objects will also protect their

privacy through clean the Relevant software search or

viewing records regularly and open the traceless

browsing settings or use some special search tools.

Because this is a semi open question, which allows the

subjects to supplement the ways to protect privacy.

They also cover the camera with objects and use

different avatars and nicknames on different platform

to do their privacy management.

According to the above data, although 72.3% of

the people think that privacy management does not

bring inconvenience to themselves. In fact, even

though privacy management will bring some troubles

to the use of the platform, some of the research objects

will still do privacy management on the algorithm

platform.

4.11 The Relationship Between Attitude

and Inconvenience Has Been

Caused by Privacy Management

In the study of the relationship between attitude and

inconvenience has been caused by privacy

management, the author uses linear regression method

to analyze. The results show that sig is 0.355b (as

shown in Table 30), which is much higher than 0.05b.

In the model summary, the R value of privacy and

attitude is + 0.096a (as shown in Table 31), and the

relationship between them is weakly positive. That is

to say, the more Chinese young people think that

privacy management on the algorithm platform will

not bring inconvenience to them, the more obvious

their attitude towards obtaining privacy information

on the algorithm platform.

Table 28: Questionnaire results- "The defensive practices I have done for protected my privacy. "

Table 29: Questionnaire results-"Privacy protection in the platform hasn't caused inconvenience for me."

Table 30 Statistical Data-ANOVAa between attitude and inconvenience has been caused by privacy management.

Table 31: Statistical Data-Model Summary between attitude and inconvenience has been caused by privacy management.

ate

ercentage

Turn off the permission of microphone, location, etc. 80 85.1

Clean the relevant software search or viewing records regularly. 75 79.8

Open the traceless browsing settings or use some special search

tools.

75 79.8

binding all activities to a single identity.

24 25.5

The others 5 5.3

ppypy

Rate Percentage Cumulative percentage

YES 68 72.3 72.3

NO 26 27.7 100.0

Total 94 100.0

yp p

Model Sum of Squares df Mean Square F Sig.

1 Regression 0.06 1 0.06 0.862 .355b

Residual 6.419 92 0.07

Total 6.479 93

ANOVAa

a Dependent Variable: Attitude

b Predictors: (Constant), No inconvenience has been caused by privacy management

Privacy Management of Chinese Youth in the Age of Algorithms

161

5 DISCUSSION AND

CONCLUSION

The vast majority of Chinese youth are worried about

privacy leakage, but a few people are not aware of the

privacy problems that algorithms may bring.

Demographic variables affect the attitude toward

privacy control. The relationship between attitude and

age, attitude and education, attitude and major are

weak and positive. However, there is almost no

relationship between attitude and gender (It is

reasonable to think that the behavioral differences

brought by gender are mostly based on the differences

in education, social discipline and resources, rather

than physiological differences. Nowadays, because of

the increasingly equal gender, the impact of gender on

behavior is gradually weakening.)

Privacy ownership affects the attitude toward

privacy control. Most youth can recognize and use

their privacy ownership in the age of algorithms, but

they think their privacy management behavior

ambitiously and don’t understand enough.

The knowledge of the result of privacy invasion

affects the attitude toward privacy control. Most of the

young people have experienced privacy leakage. Most

of their knowledge about privacy leakage comes from

the media, but they seldom discuss this topic with their

relatives and friends.

No inconvenience has been caused by privacy

management affects the attitude toward privacy

control. Some youth think that the policy management

is inconvenience, but most of them still do the privacy

management on the algorithm platform.

For this study, the author think that there are

mainly the following limitations. First, the author did

not carry out the quota, the proportion of gender and

age is not consistent with the real proportion, resulting

in the relationship between them and attitude is not

obvious. Secondly, the author thinks that the influence

of education on the attitude towards privacy

management is false, and the essence of it is the

influence the combination of age and practical

experience. In the future research, the author hopes to

conduct a more specific and extensive qualitative

research on people's specific behavior of privacy

protection through interviews.

ACKNOWLEDGMENT

Thanks for the inspiration and help of Mr. Wei FANG

from the Communication Department of Beijing

Information Science and Technology University.

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