Privacy in Social Network Sites

Privacy Risks in Social Network SitesPrioritization and Framework

David Riphagen

Social Network Sites?

1.Personal profile


1.Personal profile

2.Friends list


1.Personal profile

2.Friends list

3.Ability to view other profiles


1.Personal profile

2.Friends list

3.Ability to view other profiles

4.Membership rules


Severe Privacy Threats

for

Users of Social Network Sites

Prioritize Threats

Deconstruct Threats

by

1. Activities that cause damage

2. Reasons why activities are damaging

Threat?

Information CollectionThreat

“Joe”


“Joe”


“Joe”

Source: Riphagen, D., 2008. The Online Panopticon. Privacy Risks for Users of Social Network Sites. Identification and prioritizations of privacy rirks for users of Social Network Sites and cosniderations for policy makers to minimize these risks. , 149. Available at: www.davidriphagen.nl/Riphagen_2008_PrivacyRisksForUsersofSocialNetworkSites.pdf.

http://www.davidriphagen.nl/Riphagen_2008_PrivacyRisksForUsersofSocialNetworkSites.pdf


Source: Riphagen, D., 2008. The Online Panopticon. Privacy Risks for Users of Social Network Sites. Identification and prioritizations of privacy rirks for users of Social Network Sites and cosniderations for policy makers to minimize these risks. , 149. Available at: www.davidriphagen.nl/Riphagen_2008_PrivacyRisksForUsersofSocialNetworkSites.pdf.



Collection

“Joe”

Threat

Collection

“Joe”

Threat

Processing

Threat

Processing

Information Processing

Threat

Processing

Information Processing

ΔJoe changed his relationship

status from ‘in a relation’

to‘invisible’

Collection

“Joe”

Threat

Processing

Collection

Dissemination

“Joe”

Threat

Processing

“If you use a service from a partner, the privacy statement of that partner applies. Check their privacy statement when visiting their website.”

Threat

Information Dissemination

Source: Hyves Prvacy Policy. Available at: http://www.hyves.nl/privacy/

http://www.hyves.nl/privacy/

http://www.hyves.nl/privacy/

Threat


Source: API Methods Hyves API. Available at: http://trac.hyves-api.nl/wiki/APIMethods

http://trac.hyves-api.nl/wiki/APIMethods


Threat


4/29/08 11:50 AMAPIMethods - hyves_api - Trac

Page 14 of 85http://trac.hyves-api.nl/hyves-api/wiki/APIMethods

returnvalues

body, comment, commentid, created, currentpage, info, resultsperpage, running_milliseconds,

target_blogid, timestamp_difference, totalpages, totalresults, userid

blogs.getForFriends

Retrieves the most recent blogs for the friends of the loggedin user.

Added: Apr 17, 2008

Paginated

params

none

responsefields

This method supports the use of ha_responsefields. Acceptable values are a comma separated list

of 0 or more of the following:

commentscount

respectscount

tags

sort

Sorted by age. The most recently created items are returned first.

extra

For this function to work, you need to supply a valid access token to oauth_token.

For more information on oAuth, see APIoAuth and http://oauth.net/.

Note that retrieving something by id always succeeds (as long as the id is valid), even if the

visibility settings of the object do not allow viewing it. If some user is not allowed to view

something, he/she will not get the id from another call. See for more info.

examples

example 1

url

<body>Lovely!</body> <created>1205495604</created> </comment> <info> <timestamp_difference>0</timestamp_difference> <totalresults>2</totalresults> <totalpages>1</totalpages> <resultsperpage>2</resultsperpage> <currentpage>1</currentpage> <running_milliseconds>321</running_milliseconds> </info></blogs_getComments_result>

http://data.hyves-api.nl/?oauth_token=YXRfMTA0ODk1OV-IsZ9CSfstiNhrjkyj10-X&ha_method=blogs.getForFriends&ha_resultsperpage=2&ha_responsefields=commentscount%2Crespectscount%2Ctags&oauth_consumer_key=MV9LTucf6nmdjzU8i0obS1QP&oauth_timestamp=1208429050&oauth_nonce=552150&ha_version=1.0&oauth_signature_method=HMAC-SHA1&ha_format=xml&ha_fancylayout=false&oauth_signature=VlQ5tngL5gXTP3ruNvtqmEoAGws%3D



returnvalues

body, comment, commentid, created, currentpage, info, resultsperpage, running_milliseconds,

target_tipid, timestamp_difference, totalpages, totalresults, userid

tips.getForFriends

Retrieves the most recent tips for the friends of the loggedin user.

Added: Apr 17, 2008

Paginated

params

tipcategoryid --- Filter selecting tips by tipcategoryid.

Optional

responsefields

This method supports the use of ha_responsefields. Acceptable values are a comma separated list

of 0 or more of the following:

commentscount

respectscount

sort


extra






examples

example 1

url

result

<totalresults>2</totalresults> <totalpages>1</totalpages> <resultsperpage>2</resultsperpage> <currentpage>1</currentpage> <running_milliseconds>281</running_milliseconds> </info></tips_getComments_result>

http://data.hyves-api.nl/?oauth_token=YXRfMTA0ODk1OF8g6dsY_ZYOmj1J3x2ZFQRD&ha_method=tips.getForFriends&ha_resultsperpage=2&ha_responsefields=commentscount%2Crespectscount&oauth_consumer_key=MV9LTucf6nmdjzU8i0obS1QP&oauth_timestamp=1208429054&oauth_nonce=149096&ha_version=1.0&oauth_signature_method=HMAC-SHA1&ha_format=xml&ha_fancylayout=false&oauth_signature=lNnIN2Qeq%2Bllj%2BCoL6UmfuRo%2FcA%3D

<?xml version="1.0" encoding="UTF-8"?>



returnvalues

created, currentpage, emotion, info, resultsperpage, running_milliseconds, timestamp_difference,

totalpages, totalresults, userid, visibility, where, www, wwwid

wwws.getForFriends

Retrieves the most recent www(Who What Where)s for the friends of the loggedin user.

Paginated

params

none

sort


extra






examples

example 1

<?xml version="1.0" encoding="UTF-8"?><wwws_getByUser_result> <www> <wwwid>7cda4c7efe64b58c</wwwid> <emotion>Climbing a tree</emotion> <where>jungle</where> <userid>738a3e92186fe5e9</userid> <visibility>superpublic</visibility> <created>1205496045</created> </www> <www> <wwwid>a08d0f76c34ea081</wwwid> <emotion>Distracting Gorilla</emotion> <where>jungle</where> <userid>738a3e92186fe5e9</userid> <visibility>superpublic</visibility> <created>1205496004</created> </www> <info> <timestamp_difference>0</timestamp_difference> <totalresults>3</totalresults> <totalpages>2</totalpages> <resultsperpage>2</resultsperpage> <currentpage>1</currentpage> <running_milliseconds>297</running_milliseconds> </info></wwws_getByUser_result>

Source: API Methods Hyves API. Available at: http://trac.hyves-api.nl/wiki/APIMethods



Collection

Processing

Dissemination

Incident?

Incident

Threat

Survey

• American privacy and Internet experts

• Identify privacy incidents

• How much damage incurred?

• How many users affected?

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Damage?

Collection

Processing

Dissemination

Incident

Threat

Damage

How is this Damaging?

Incident

Threat

Damage

1. Information-based harm


Incident

Threat

Damage


2. Informational inequality


Incident

Threat

Damage



3. Informational injustice


Incident

Threat

Damage

4. Restriction of moral autonomy /

Inability to create moral identity



3. Informational injustice


Recovery?

The Online Panopticon

MySpace. This information, and especially the final remark, was posted with

the intent to harm Megan. Solove (2008d) states that it is hard to prove that

these remarks led directly to the suicide. However, it is very clear that the

remarks were made to harm Megan, and therefore part of information-based

harm.

Information-based

harm

Information

inequality

Informational

injustice

Moral

autonomy and

identification

Information

collection

3. Harmful remarks

towards Megan are

uploaded to

MySpace (collected).

Information

processing

Information

dissemination

1. Disclosure of

Megan's profile ID

makes contacting

her possible.

2. An adult, from a

different social

sphere, contacts

Megan.

Table 7: Framework applied to Megan Meier case

With this analysis, I have showed that the specific harms in the Megan Meier case

derive from the possibilities that identity-relevant information is used for harm, and the

movement of this information through different spheres.

I DENT ITY THEFT

In a research project on identity theft, security software manufacturer Sophos created a

fake Facebook profile, Freddi Staur, and asked 200 Facebook members to become his

friend (Sophos 2007). 41% of the users that they contacted gave away identity-relevant

information such as email address, date of birth and phone number to a green frog

called Freddi Staur, who divulged minimal information about himself (Sophos 2007).

Graham Cluley, senior technology consultant at Sophos, says "while accepting friend

requests is unlikely to result directly in theft, it is an enabler, giving cybercriminals many

of the building blocks they need to spoof identities, to gain access to online user

accounts, or potentially, to infiltrate their employers' computer networks." (Sophos

2007). Experts agree that identity theft is one of the most harmful activities a SNS user

could encounter.

That the information on SNS is easily accessible and can be used by stalkers is

something Samer Elatrash experienced in real life (Elatrash 2007). He is, within certain

groups, a well-known pro-Palestinian Israeli who signed up for Facebook because his

friends asked him to. After two weeks, he found out that someone copied information

124

Incident

Threat

Damage

Recovery






harm.

Information-based

harm

Information

inequality

Informational

injustice

Moral

autonomy and

identification

Information

collection

3. Harmful remarks

towards Megan are

uploaded to


Information

processing

Information

dissemination

1. Disclosure of

Megan's profile ID

makes contacting

her possible.

2. An adult, from a

different social

sphere, contacts

Megan.





I DENT ITY THEFT











could encounter.





124

Incident

Threat

Damage

Recovery






harm.

Information-based

harm

Information

inequality

Informational

injustice

Moral

autonomy and

identification

Information

collection

3. Harmful remarks

towards Megan are

uploaded to


Information

processing

Information

dissemination

1. Disclosure of

Megan's profile ID

makes contacting

her possible.

2. An adult, from a

different social

sphere, contacts

Megan.





I DENT ITY THEFT











could encounter.





124

Incident

Threat

Damage

Recovery






harm.

Information-based

harm

Information

inequality

Informational

injustice

Moral

autonomy and

identification

Information

collection

3. Harmful remarks

towards Megan are

uploaded to


Information

processing

Information

dissemination

1. Disclosure of

Megan's profile ID

makes contacting

her possible.

2. An adult, from a

different social

sphere, contacts

Megan.





I DENT ITY THEFT











could encounter.





124

Incident

Threat

Damage

Recovery






harm.

Information-based

harm

Information

inequality

Informational

injustice

Moral

autonomy and

identification

Information

collection

3. Harmful remarks

towards Megan are

uploaded to


Information

processing

Information

dissemination

1. Disclosure of

Megan's profile ID

makes contacting

her possible.

2. An adult, from a

different social

sphere, contacts

Megan.





I DENT ITY THEFT











could encounter.





124

Incident

Threat

Damage

Recovery

Severe Privacy Threats

for

Users of Social Network Sites

Prioritize Threats

Deconstruct Threats

by

1. Activities that cause damage

2. Reasons why activities are damaging

Privacy Risks in Social Network SitesQuestions?

Back-up

Research Activities

LiteratureReview

1

DeskResearch

2Facebook

Case Study

3

Expert Survey

4

Research Activities

“Joe”

Threat

Collection

“Joe”

Threat

Collection

Processing

Dissemination

Privacy threats in Social Network Sites

should be conceptualized as

unwanted access to

Identity-Relevant Information

Collection

Processing

Dissemination

The main incident that puts the

privacy of users of SNS at risk is:

The collection of information from

secondary sources,

Which is used by the government

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SNS restricts access to

information

SNS leaves access to

information open

User does not join SNS User: ( 0 ) - ( 0 ) = ( 0 )

SNS: ( 0 ) - ( 1 ) = ( -1 )

User: ( 0 ) - ( 1 ) = ( -1 )

SNS: ( 0 ) - ( 0 ) = ( 0 )

User does join SNS User: ( 1 ) - ( 0 ) = ( 1 )

SNS: ( 1 ) - ( 1 ) = ( 0 )

User: ( 1 ) - ( 2 ) = ( -1 )

SNS: ( 1 ) - ( 0 ) = ( 1 )

Table 1: options with pay-offs for Social Network Sites and users.

The model is based on game theory, a science that investigates options and

outcomes of multi-actor situations in the terms of alternatives with different pay-offs.

Users always derive benefits from joining a SNS in terms of increased social contact,

therefore this option always scores (1). However, users are also subject to ‘tagging’ of

their photographs and discussions about them if they are not members of SNS

(ENISA 2007). If a SNS restricts this form of information uploading without consent,

users do not experience the drawbacks from this. When a users joins a SNS and there

are no restrictions on collection, processing and dissemination of their information,

they will even experience more harm. Social Network Sites do not derive any benefits if

users do not join the network, and restricting access to the information of users costs

them money. With this pay-off, SNS have no incentive to restrict access to the

information, while users have incentives to join the network. Although users seem

indifferent between joining or not joining when the access to the information is not

restricted, I argue that users are not familiar enough with the negative consequences of

joining the network and will therefore choose to join the network.

This simple model suggests that SNS can implement measures that better protect

identity-relevant information if these measures would not contradict with creating

monetary benefits from the information. Users want the benefits from joining a SNS

without the privacy risks. However, many users are unaware of these risks and it it

therefore unknown if they would be willing to pay for better privacy protection,

thereby creating revenue for SNS from other sources. This should be investigated more

thoroughly. Other actors also have an incentive to decrease the privacy risks in SNS

because of the negative connotation they generate. All of the partners of the SNS

(Beacon, advertisement) have an incentive to prevent this negative connotation of SNS,

because this could be coupled to them. Therefore, they want to minimize the negative

exposure of SNS related to privacy harms. As these partners have monetary means to

contribute to the decrease of privacy risks, this could pose a viable solution for the

problem.

The actor analysis of the value chain of SNS shows that many different actors are

involved that all have a financial incentive. Social Network Sites currently have no

incentives to restrict the access to identity-relevant information. Furthermore, users

have incentives to become members of SNS, but get confronted with the negative

effects of privacy threats. Governments and privacy regulators have incentives to

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4. Because the data subject is unaware of how this algorithm works, a power

relationship exists between Facebook and the user. The user might not want

some information to be collected or disseminated, but has no influence on

this. This is clearly against the OECD's individual participation principle

(Gellman 2008).

5. The collected and processed information is disseminated to some people in

the friends list and could cross different social spheres without the consent of

the user. In this scenario, your beer drinking soccer friends could become

aware of your academic life, or worse, the other way around.

6. This information could be embarrassing, and could harm the user once it is

disseminated to wrongdoers.

7. Because the user has no influence on which information is being sent to

whom, it restricts him in his moral autonomy. This differs from the harms

under (3) and (4), because it does not prohibit users from crafting their moral

identity, but from controlling the dissemination of their identity to others.

Information-

based harm

Information

inequality

Informational

injustice

Moral autonomy

and

identification

Information

collection

2. Information

collection takes

place without

informed consent

and this

information could

be embarrassing

to users.

1. Information

collected from

third-party

website, other

social sphere.

Information

processing

4. Users are

unaware of how

this algorithm

works.

3.Algorithm

defines to whom

the information

will be

disseminated.

Information

dissemination

6. Information

could be used to

harm user, for

example

embarrassing

information.

5. Information is

being

disseminated to

friends, in

various social

spheres.

7. User not able to

build his own

moral biography.

Table 5: Framework applied to Beacon case

118






harm.

Information-based

harm

Information

inequality

Informational

injustice

Moral

autonomy and

identification

Information

collection

3. Harmful remarks

towards Megan are

uploaded to


Information

processing

Information

dissemination

1. Disclosure of

Megan's profile ID

makes contacting

her possible.

2. An adult, from a

different social

sphere, contacts

Megan.





I DENT ITY THEFT











could encounter.





124

Information-

based harm

Information

inequality

Informational

injustice

Moral autonomy

and identification

Information

collection

Information

processing

Information

dissemination

1. More

information is

disclosed than

needed to third

parties, even

sensitive

information.

2. Users cannot

expect that a birthday

application gets access

to photos, because

this information

resides in different

social spheres.

Table 11: Framework applied to Facebook Third-party Applications case

The Facebook Third-Party Applications case shows that users' expectations of

privacy and expectations of an application’s function on Social Network Sites is very

different from what happens in reality. Also, it shows that SNS like Facebook do not

have the same standard of security on every part or function of their website.

UNWANTED D ISSEM INAT ION OF I NFORMATION TO OTHERS / GROUPS

Oxford University in England has a disciplinary body, the proctors, which has admitted

to using Facebook in order to find evidence of students breaching the University's code

of conduct (Gosden 2007). A common post-exam tradition at Oxford is 'thrashing', or

covering your friends with flour, confetti or even raw meat or octopus. The University

does not approve of this and students have been receiving fines of 100 pounds and a

prohibition from graduating (Knight 2007).

Recently, students that have posted pictures of these activities on their Facebook

profiles have been disciplined. Of course the actions of these students are against the

code of conduct of the University, but the question here is why the students got so

upset when the University used this information to discipline them. As one of the

students, Alex Hill, says: “I don't know how this happened, especially as my privacy

settings were such that only my friends and students in my networks could view my

photos. It's quite unbelievable and I am very pissed off, [I] just hope that no-one else

gets 'caught' in this way.” Part of the amazement comes from the fact that the students

did not expect that the University would be able to access the information on the SNS.

Experts agree that the unwanted dissemination of information to others is something

that happens on a large scale.

1. The students posted information online, only for their friends and fellow

students to see. Because Facebook started as a SNS for college students only,

it created the image of being only accessible to students from the same

131

Privacy in Social Network Sites

Technology