Disclosing personal information in online social network services is a
double-edged sword. Information exposure is usually a plus, even a must, if
people want to participate in social communities; however, leakage of
personal information, especially one's identity, may invite malicious
attacks from the real world and cyberspace, such as stalking, reputation
slander, personalized spamming and phishing.
Even if people do not reveal their personal information online, others
may do so. In this paper, we consider the problem of involuntary
information leakage in social network services and demonstrate its
seriousness with a case study of Wretch, the biggest social network site
in Taiwan. Wretch allows users to annotate their friends'
profiles with a one-line description, from which a friend's private
information, such as real name, age, and school attendance
records, may be inferred without the information owner's knowledge. Our
analysis results show that users' efforts to protect their privacy
cannot prevent their personal information from being revealed online.
In 592,548 effective profiles that we collected, the first name of
72% of the accounts and the full name of 30% of the accounts could
be easily inferred by using a number of heuristics. The age of 15%
of the account holders and at least one school attended by 42% of the
holders could also be inferred. We discuss several potential means of
mitigating the identified involuntary information leakage problem.
Social network services (SNS) represent one of the most important
applications of the Internet in recent years, with some SNSs hosting
millions of profiles, for example, Myspace, Facebook, Flickr, Orkut,
and Yahoo! 360. Such services provide a virtual playground for
participants to meet new friends, maintain contact with
acquaintances, and share resources with others over the Internet. To
let others know about themselves, users normally publish personal
information online, such as their appearance, nationality, school
attendance records, work experience, and hobbies. The information not
only lets people know more about a person, but also enables others to
find the user through web searches. Thus, users are normally
encouraged to disclose personal information in order to receive
higher exposure (more "eyeball counts" in Internet jargon) in the
Disclosing personal information online is a double-edged sword. Information
exposure is usually a plus, even a must, if people want to join certain
types of social communities; however, leakage of personal information,
especially one's identity, may invite malicious attacks from the real world
and cyberspace. Many studies have addressed the problems of privacy
invasion and security threats raised by information exposure online, e.g.,
stalking, reputation slander, personalized spamming and phishing. We
discuss some of the threats in Section 6.1 and
refer interested readers to .
Even if people do not reveal their personal information, others
may do so. We illustrate how this could happen with the following
example. Suppose Alice uses the pseudonym boulder_987 to
protect her identity. Bob, a friend of Alice, may reveal Alice's age,
occupation, and even her real name during their interaction in the
Bob may recommend2 Alice as "the best steak chef in
Boston" on his own page, and thereby inadvertently reveal Alice's
occupation and the city she lives in.
Suppose Bob also uploaded a photo taken with Alice to his online
albums. He may annotate the photo with "Bob and Alice Lewis at
George's Wedding" and link the photo to Alice's profile. This action
would reveal Alice's full name and the fact that Bob, Alice, and George
know each other.
Thus, Bob may unintentionally reveal a great deal of information about
Alice without her knowledge. In other words, Alice's efforts to protect
her identity may be easily nullified by others' behavior. Moreover, it
is difficult to detect occurrences of such leakages due to their
distributed nature. This problem, which we call involuntary
information leakage, is becoming a serious threat to privacy because of
the popularity of social network services.
In this paper, we investigate the extent of Involuntary Information Leakage in Social Network Services.
We analyze data gathered from Wretch, the biggest social network site
in Taiwan. The data set contains 592,548 effective profiles, the
social connections between the profiles, and annotations describing
the social connections.
To quantify the degree of such leakages, we attempt to infer the real
name, age, and school attendance records of each user based on
annotations made by friends. Our results show that the first name of
72% of users and the full name of 30% of users can be easily
inferred by a number of heuristics. The age of 15% of users and
at least one school of 42% of users can also be inferred. The high
ratio of information leakage evidences that users tend to annotate
their friends by using real names and by describing their offline
relationships. Based on our analysis results, we consider several
possible ways of mitigating the identified involuntary information
The remainder of this paper is organized as follows. In
Section 2 we provide an overview of earlier studies
related to social networks and online privacy. In
Section 3, we describe our data collection procedures and
examine the demography and levels of self-disclosure. We investigate the
leakage of real names in Section 4 and the leakage of
age and school attendance records in Section 5.
Section 6 considers threats and risks that may occur
due to information leakage and potential solutions to the problem. Then,
in Section 7 we present our conclusions.
2 Related Work
Online social network services have attracted the attention of both
entrepreneurs and researchers in recent years . From an
academic perspective, the services provide the research community with an
unprecedented opportunity to analyze the structure and properties of online
social networks on a large scale.
Ahn et al. analyzed the structure of online social networks and found that
they have many similarities with offline social networks .
Mislove et al. conducted a large-scale study of snapshot graph structures
of online social networks. They compared the structures of online social
networks and the Web, and validated online social networks with the
structural properties of offline social networks . Kumar et
al. analyzed the evolution of two popular online social networks, and
identified the high-connectivity core and the star structure of each
network . O'Murchu et al. compared and classified different
categories of social and business networking communities .
User interaction and relationships in social network services have also
been investigated by researchers. Boyd analyzed social network services
from the perspective of human factors, and found that knowledge of, or
trust between, users is not required to establish online
relationships . Moreover, it has been shown that online
social networks engender much weaker ties between users than their offline
Based on 4,000 profiles gathered from Facebook.com, Gross et al.
analyzed the degree of information disclosure and the subsequent risks.
They found that "personal data is generously provided, and
limiting privacy preferences are hardly used" , while
Acquisti observed that "technology alone or awareness alone may
not address the heart of the privacy problem" . The
privacy issues raised by social network services present a difficult
challenge to both information technology and social science researchers.
3 Data Description
In this section, we begin with an introduction to Wretch, the social
network service we studied, and then describe our data collection
Wretch (http://www.wretch.cc) was established in 1999 and
acquired by Yahoo! Taiwan in 2006. It is currently the most popular
social networking site in Taiwan. At the time of writing (Feb 2008), it
hosted about 4 million profiles.
Like other social network systems, Wretch provides an array of services,
including albums, blogs, a bulletin board system (BBS), video sharing,
and a discussion forum. Anyone can freely browse all the profiles on
Wretch without an account. Joining the service as a registered member is
free. Members can upgrade their service levels with a yearly
subscription to obtain a larger storage space and more functionalities.
3.2 Data Collection
To collect users' profiles and information about their social relations, we
developed a crawler program to fetch profile pages from Wretch. We began
the data crawling with an initial set of accounts. In each round, the
crawler fetched an account's profile and its friend list; and HTML
processing techniques were employed to extract the desired information. If
the friend list contained an account the crawler had not seen before, it
was added to the job queue. At the end of each round, the crawler randomly
selected an account from the queue, and targeted that account in the next
The crawler continued fetching users' data until the job queue was
We collected the data in September 2007.
In sum, we fetched 766,972 profiles, which constituted 20% of
Wretch's population at the time. As we focus on name leakages caused
by friends, profiles with an empty friend list are irrelevant to our
study. Therefore, we removed profiles that did not have any outgoing
friend connections. This yielded a reduced set of 592,548 profiles,
corresponding to 15% of the population.
Our data set is summarized in Table 1.
Table 1: Overview of crawled data
Number of users
Number of Effective users
Number of Connections
Avg Connections per user
3.3 Self-Information Disclosure
Self-disclosure is defined as "telling others previously unknown
knowledge so that it becomes shared knowledge" . It is
normally intended to increase understanding between people, build trust,
strengthen the ties between people, and bind romantic relationships or
To determine the degree that Wretch users reveal information about
themselves, we summarize the disclosure ratio of personal statistics in
Fig. 1. We observe that most users provide
gender and birthday information, and many list their email addresses. In
addition, details of instant messaging accounts (e.g., MSN Messenger and
Yahoo Messenger) are often given, with a disclosure ratio higher than
30%. This is not surprising as real-time messaging applications are now
one of the main communication methods used by young
Figure 1: Ratio of self-disclosure. The gender and
birthday fields have a high disclosure
We define the degree of self-disclosure (DSD) in order to
quantify a user's tendency to disclose his/her own personal information.
A user's DSD is defined as follows:
n ∑ i = 1
where n is the total number of fields, Fi is a binary value indicating
whether field i has been completed, and Wi is the weight of field i.
We compute Wi by the following equation:
Wi = 1 −
n ∑ j = 1
where Ri is the disclosure ratio of field i. The ratio is
computed by dividing the number of users who complete field i by the
total number of users. For example, if there are 1,000 users, and 100
of them provided the weight information, the disclosure ratio of the field
"weight" would be 100 / 1000 = 0.1. We compute the DSD for all fields,
except nicknames, introductions, and instant messaging accounts, as the
account fields may not be applicable to every user.
4 Involuntary Name Leakage
On Wretch, a user can provide a free-form text (limited to one line)
to annotate a friend's profile, which we call friend
annotations (or descriptions).
However, people invent their own ways of using
this field. After viewing thousands of annotations, we identified
some typical patterns and found that a typical annotation is
comprised of three parts: 1) a tag, which is used for
classification; 2) the real name or nickname of the
friend to be annotated; and 3) a description of the friend's features
or the relationship between the two friends. Some examples are
"∗Beauty∗ Cathy Brown - The hottest girl of
High School" and "[[School Mate]] Tony MY BUDDY."
In these ways, a great deal of personal information could be revealed
through friend annotations without the information owner's knowledge.
(Hereafter, we refer to the information owner as the annotatee.)
For example, if we find 5 incoming annotations for a user contain
the substring "Jane Garcia" and 10 descriptions contain "Jane,"
then we may safely guess that the annotatee's real name is Jane
4.1 Inference Methodology
To infer the real name of a profile, we first collect all of its incoming
annotations, i.e., those that the user's friends compile for him/her. Then,
from each description, we extract name candidate tokens from the text as
We break the text into disconnected tokens by using several types
of delimiters, for example:
symbols: < SPACE > , < TAB > , #
punctuation marks: ' " , . ( ) [ ]
As most Wretch clients use Chinese, we employ Chinese-specific naming
rules to determine whether a delimited token is potentially a Chinese name.
A Chinese name is usually composed of two or three characters3
- a one-character family name, e.g., Chen, Wang, Lin, and a one- or
two-character first (given) name, e.g., Xin, Kuan-Ta, Ieng-Fat. Thus, if
a token contains two or three characters, it could be a Chinese first
name or full name, and we consider it as a real name candidate token.
We associate each name candidate token with a duplication
count. For example, if a name candidate token C1 appears in
annotations from three friends, the duplication count of C1 will be
We summarize the statistics of friend annotations and extracted name
candidate tokens in Table 2. In our data set,
we find that, on average, a user has 7 online friends and 6.8
incoming annotations from them.
Table 2: Summary statistics
of friend annotations and extracted name candidate tokens.
Friend Annotations and Name Candidates
Avg In-Degree with Annot.
Avg In-Degree with Dup. Tokens
Avg # Unique Name Candidates
Although the friend description is not a required field, 96% of the
connections are annotated. In addition, 49% of the incoming
annotations for a user contain at least one name candidate token that
appears in other annotations for the same user. For each user, we
extract an average of 3.8 unique name candidate tokens, which will
serve as our input for real name inference.
4.1.1 Inference of Full Names
Given the extracted name candidate tokens for a user, we apply
the following heuristic rules to infer the user's full real
Common Family Name: We consider a token as a full real
name if 1) its first character is a common family name (according to the
100 family names listed in ); and 2) its duplication
count is greater than 1.
First Name as a Substring of the Full Name: We consider a
token Ci as a full real name if 1) there is another token Cj equal
to Ci without its first character; and 2) the duplication count of
Ci is greater than 1. For example, if Ci is "Wang Ta-Ming,"
Cj is "Ta-Ming," and Ci appears more than once, then we consider
that Ci is probably the full real name of the user.
Common Full Name: We consider a token as a full real name if
it was one of the 574,010 names on the enrollment list for the national
college exams for the years 1994 to 2007 .
Nickname Decomposition: In Chinese, it is common for a
person to have a nickname that is derived from his/her family or given
names. For example, a man called Wang Ta-Ming may have a nickname like
"Old Wang," "Bro Wang," "Bro Ta," "Little Ming," or "Bro
Ta-Ming." Generally, for a person with a name in the format "FN
GN1-GN2," some possible nicknames could be:
where X could be FN, GN1, GN2, or GN1-GN2. We examined the nicknames that
appeared in our data set and manually picked 38 common prefixes and
postfixes for nickname composition.
We consider that a token Ci is a full real name if 1) it contains the
predefined nickname prefixes or postfixes as specified; and 2) after
removing the corresponding prefix or postfix, the remaining part is the
same as other name candidate tokens.
Common Word Removal:
If we do not find any matched name candidate based on the above rules, a
name candidate token is considered as a full real name if 1) its
duplication count is greater than 1; 2) it does not contain any
nickname-composition prefix or postfix; and 3) it does not contain any
general word that people use in daily life, e.g., he, she, friend, lover,
classmate, good, or bad. The removal of common words is based on a
dictionary containing 100,511 words . If more than one
name candidate matches these criteria, then the one with the highest
duplication count is deemed the real name of the user.
4.1.2 Inference of First Names
The method used to infer first names is the same as that used for full
names, except for the following three points. 1) We use first name
candidates (two characters) instead of real name candidates (three
characters). 2) Because heuristics 1 and 2 are used specifically for full
name inference, we only use heuristics 3, 4, and 5 for the first name. 3) A
common first name table (comprising 208,581 names) is used in heuristic 3
instead of the common full name table . Also, because
users may include the names of other people, such as "Dolly's sister," we
only apply rule 3 to name candidate tokens that have a duplication count
greater than 1.
4.2 Inference Results
Here, we summarize the results of the name inference procedures.
Table 3 lists the ratio of users whose real
names we were able to infer. We successfully inferred first names for
72% of users, and full names for 30% of users. If we count both
first names and full names, totally 78% of users are subject to
the risk of name leakage. In addition, we detail the inference
success ratio of each heuristic rule for real names and first names
in Table 4.
Table 3: Ratios of Correctly Inferred Names.
Type of name
Ratio of Name Inference
Real name or first name
Table 4: Ratio of Users Whose Names Can be Inferred, by Different
Common family name
Relation of first name
Common full/first name
Relation of nickname
Removal of common words
A complete validation of our name inference results was not possible
because we did not know the true real names of the users. Therefore, we
employed manual validation. By randomly selecting 1,000 profiles, and
examining the real names we inferred for them. We believe that at least
738 of the selected names are correct. The majority of cases of
incorrectly inferred names are caused by mistaking a user's nickname for
the real name, as we cannot enumerate all possible prefixes and postfixes
for nicknames derived from real names. Moreover, our methods cannot
distinguish a nickname from a real name if the former is not a literal
derivative of the latter.
We acknowledge that our proposed heuristics cannot always derive correct
real names. However, exact real name inference is not our primary goal.
Instead, we seek to verify the qualitative fact that "involuntary
real name leakage occurs in real-life social network systems, and the
degree of leakage is significant." In this way, our inference
results, though not very accurate, are sufficient to support our
4.4 Demographic Analysis
Figure 2: Ratio of name leakage based on
Figure 3: Ratio of name
leakage based on age.
Fig. 2 shows that males are susceptible
to higher full name leakage risks than females. However, the first
names of males are less likely to be released involuntarily than
those of females. This interesting finding implies that people are
more likely to use full names to describe a male friend and first
names to describe a female friend. Fig. 3
shows that the risk of name leakage is lower for older users. This
phenomenon can be simply explained by the number of friends that
people have in the social network. Because the 16-25 age group
constitutes the core population of Wretch and social connections are
mostly between users of similar age, younger people tend to have more
online friends than other age groups. Therefore, they have more
incoming friend annotations and a higher risk of name leakage than
users in other age groups.
4.5 Risk Analysis
To confirm that identity leakage is definitely involuntary, we
checked whether users whose names were inferred correctly had disclosed
their real names in their profiles. We found that less than 0.1% of
those users had voluntarily revealed their real names (either their full
name or first name) in their profiles. This supports our contention
that name leakage is caused by annotations made by online friends,
rather than self-disclosure.
To determine the source of name leakage, i.e., who reveals the identities
of other users, we investigate the usage of real names in friend
annotations. We define two metrics to quantify the tendency to use real
names when describing online friends:
Degree of Using Real Name (DUR): DUR quantifies a
person's tendency to use real names when annotating his/her friends'
profiles. It is computed as the ratio of that person's outgoing
annotations that contain the annotatee's real name.
Degree of Being Called by Real Name (DCR): DCR quantifies
the extent that a user is annotated with his/her real name by online
friends. It is computed as the ratio of incoming annotations containing
the user's real name.
Figure 4: DUR vs DCR. DCR has a positive relation to
Figure 5: DCR and DUR distribution over
We first investigate whether real name use behavior is
symmetric, i.e., are DCR and DUR positively correlated?
Fig. 4 suggests that there is a consistent
positive correlation between the two metrics. The result indicates
that users who receive annotations containing their real names also
tend to use real names when sending friend annotations. We also
consider the impact of the degree of self-disclosure (DSD) on DCR and
DUR, as shown in Fig. 5. Even though both DCR
and DUR have a slight positive relationship with DSD, the correlation
is insignificant, which indicates that name leakage is not strongly
related to self-disclosure. That is, friends may still reveal a
user's real name unintentionally no matter how much the user tries to
protect his/her identity online.
5 Involuntary Leakage of Age and Education Records
In online social networks, the connections between people are usually a
duplicate of their offline relationships, which suggests that
people have common attributes. For example, if the relationship
between two people is described as "classmates," they should study in
the same school, live in nearby locations, and be a similar age;
likewise, the relationship "colleague" implies that two people work in
the same organization and probably have similar professions.
If we know two users are classmates in a primary school and one of
them disclosed his/her age in the profile, we can infer that the
other one is a similar age.
To assess the
risk of personal information being revealed involuntarily, in the
following, we infer the age and education records of users who did
not provide this information. In our data set, 56% (331,827) of
users disclosed their ages, but only 10% (59,255) disclosed
their current schools in their profiles. By applying a heuristic
inference method, we successfully inferred the ages for 18% of
those who did not disclose their ages (15% of the total number of
accounts), and inferred at least one school for each of 42% of
users in our data set.
5.1 Inference Methodology
5.1.1 Inferring Age
Our inference procedures are executed in a round-based manner.
Initially, a job queue is filled with all the accounts that provide age
information. In each round, we fetch an account X from the queue, and
check all of its incoming and outgoing friend annotations. If we
determine that a user Y on X's friend list should be the same age,
we set the age field of Y equal to that of X and add Y to the job
queue. Our heuristic rule for "same age" is based on the offline
relationships of "classmate" or "schoolmate in the same year." We
identify such relationships by searching with keywords like
"classmate", "class leader", "same class," and "same grade" in
friend annotations. The inference procedure continues until the job
queue is empty.
5.1.2 Inferring Education Records
The inference methodology for education records is similar to that for
age inference except for three points. First, while a person's age is
unique, his/her education records will include information about
attending several schools. For simplicity, we assume that each user
attended at most one school in each of the four education levels,
namely, elementary school, junior high school, senior high school, and
college. Second, while users are unlikely to specify a friend's age in
annotations, they may use a school's name as a category name. Thus, we
can infer not only if two users attended the same school, but also the
name of a school the annotatee ever enrolled in. Third, in addition to
school names and offline relationships like "classmate" and
"schoolmate," we can determine if two users ever studied in the same
school by keywords like "same school," "same college," or
Figure 6: Inference results for users'
Figure 7: Inference results for users'
5.2 Inference Results
Here we summarize the inference results of age and education records.
The upper graph of Fig. 6 shows that the success ratio
for age inference is higher for users in the 16-25 year age group.
In sum, we successfully inferred the ages of 15% of users, which
corresponds to 18% of the users who did not provide age information.
The lower graph suggests that the average hop count required for
inference is about 1.2, which indicates that, in most cases, the
inferred age of a user is directly propagated the profile of a user who
discloses his/her own age.
Fig. 7 shows the inference results for users' education
records. The success ratios for school inference were approximately
20% for all education levels, except elementary school. Totally, we
inferred at least one school for 42% of users in the data set. This
success ratio is reasonably high, as only 5% and 9% of users
self-disclosed their respective junior high and senior high schools. The
results suggest that many Wretch users are linked by relationships
established in high school. The lower graph in Fig. 7
shows that the average hop count required for inference decreases by
education level, which implies that users at higher education levels
have stronger connections with their schoolmates online.
Figure 8: The inferred age differences between
pairs of self-disclosed schoolmates in the four education
Figure 9: The self-disclosed age
differences between pairs of inferred schoolmates in the four
We apply a cross-validation approach to verify the
inferred ages and education records. That is, we verify the inferred
ages based on the self-disclosed school information, and verify the
inferred school names based on the self-disclosed ages.
To verify if the inferred age information is correct, we find all the
users who satisfy the following three criteria: 1) they disclose their
current schools, 2) they do not disclose their ages, and 3) we inferred
their ages by the above methodology. Intuitively, if two people
currently attend the same school, their ages should be similar. Thus, we
computed the age differences between pairs of schoolmates, and
identified a total of 208,086 valid pairs. We summarize the age
differences between pairs of users at different education levels in
We verify the correctness of the inferred education records based on the
same intuition. However, the users being compared should satisfy the
following criteria: 1) they disclose their ages, 2) they do not disclose
their current schools, and 3) we inferred their education records. In
this case, 42,896 pairs of users who described their relationship as
"schoolmates" were identified. The distributions of the age difference
between each pair of users are shown in Fig. 9.
Both plots show that the average age differences between pairs of users
in the four education levels are generally less than 2 years. This
result suggests that our inference results for users' ages and education
records are accurate. It also confirms our conjecture that significant
involuntary information leakage occurs in real-world social network
In this section, we discuss the problems that may be caused by identity
leakage, such as personalized email spams and spear phishing, and
suggest several ways to mitigate the involuntary identity leakage
6.1 Threats Caused by Name Leakage
Spamming. Spam mail has become a major problem in recent years. It
is actually a business activity whereby spammers send emails with product
information based on massive email address lists. In effect, anyone who has
an email address is regarded as a potential customer .
To protect users from spamming, academia and industry have developed
a number of anti-spam mechanisms. One method of protection against
spam involves using a white
list[16,17,18], so that emails
from trusted parties will not be mistaken for spam. However, as
spamming is profitable, spammers are always devising new ways to
penetrate spam filters. To combat the white list approach, spammers
collect email addresses and information about social relationships
from social network services .
In our data set, 46% of users disclose a well-formed email address
that spammers may use to send spam mail as if it has been sent by one
of the target's friends. In this way, spam mail can bypass the filter
and be delivered to the target's mailbox.
Spammers may make
use of inferred real names in two ways. 1) They may use the
recipient's real name in the mail's content. 2) They may make spam
mails look like they have been sent by a friend of the target by
using the friend's real name. Consider an email containing the
recipient's full real name, where the sender is specified as a friend
with his/her correct email address and full real name. The user may
have difficulty verifying the email's authenticity.
Phishing. Similar difficulties also exist with respect to
phishing detection. Phishers normally send emails, which contain a link
to a forged web page, to obtain people's sensitive information, such as
an account ID, social security number or credit card
number . Some phishing-targeted companies, including eBay
and PayPal, and Internet security vendors provide guidelines for
recognizing phishing emails. Common rules include "checking if
the email includes your real name because phishers do not have personal
information" [20,21]. However, the assumption
that phishers do not have personal information might be incorrect as
self-disclosure is becoming more frequent in social network services.
The involuntary name leakage problem will exacerbate the problem
further, as it will be more difficult for users and phishing detection
mechanisms [22,19,23] to verify the authenticity of
a web page.
6.2 Potential Solutions
We consider three possible ways to mitigate the problem of involuntary name
leakage in social networking services.
A. Personal Privacy Settings. Social network service
providers should provide the following preference settings for every
account, no matter whether it is free or not:
options to hide personal information;
options to hide social connections (the level of connections to be
hidden should be configurable, e.g., direct friends or friends of
options to prevent a user's friends annotating the user's profile
with certain words, or deny any incoming annotation completely.
options to deny specific people access to a user's incoming and/or
B. Browsing Scope Settings. It is recommended that social
network service providers limit the profile browsing scope of users. For
example, a user could be restricted to browsing friend annotations made by
users who are at most two degrees away. One common way to limit browsing
scope is through group partitioning, i.e., only users belonging to a
certain group, where the action of joining the group requires the approval
of a moderator, can access more sensitive information about users in the
same group. Such mechanisms could prevent malicious parties from
downloading users' personal information, social connections, and
annotations in an automated way. We believe that this is the key to
solving the problem of large-scale information leakage in social network
C. Owner's Confirmation. Every operation involving
user information should be confirmed by the information owner. For
example, friend annotations should only be shown if the annotatee
agrees. This would at least prevent unintentional personal information
leakage by the user's friends.
In this paper, we consider the involuntary information leakage problem
in social network services. To assess the seriousness of the problem,
we conduct a case study of Wretch, the most popular social network
service in Taiwan. Because Wretch users are allowed to annotate their
friends' profiles with a one-line, free-form description, sensitive
information, such as a person's real name, can be disclosed without the
annotatee's knowledge. We show that 78% of users in our data set were
subject to involuntary name leakage. In addition, the ages of 15% of
users and the school attendance records (partial or complete) of 42%
of users could be easily inferred using a simple heuristic. We also show
that the risk of information leakage is not related to the degree of
self-disclosure; thus, telling a user not to disclose his/her personal
information is not an effective way to reduce the risk that his/her
identity could be revealed by online friends.
We discuss three possible ways to mitigate the identified leakage
problem, namely, providing personal privacy settings, regulating the
browsing scope, and requiring an owner's authorization to release
personal information. However, as most users do not change the
default settings, we believe that the default mechanisms provided by
the services are the most effective methods available. We suggest
operators should at least mandate that annotations do not contain any
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1. This work was supported in part by Taiwan Information
Security Center (TWISC), National Science Council under the grants
NSC 97-2219-E-001-001 and NSC 97-2219-E-011-006. It was also
supported in part by Taiwan E-learning and Digital Archives Programs
(TELDAP) sponsored by the National Science Council of Taiwan under
NSC Grants: NSC 96-3113-H-001-010, NSC 96-3113-H-001-011 and NSC
2. Many SNSs provide a
recommendation/endorsement system in which a user can "recommend"
another user to the public.
rare Chinese family names are comprised of two characters, e.g., Ouhyoung;
thus, a four-character full name is possible. However, to avoid false
identification of real names, we only consider two- or three-character
Sheng-Wei Chen (also known as Kuan-Ta Chen) http://www.iis.sinica.edu.tw/~swc
Last Update June 18, 2017