To Join or Not to Join: The Illusion of Privacy in social Networks with

Mixed Public and Private User Profiles

Paper by Elena Zhelea and Lise Getoor

Introduction• What do we want to find out about a “Private”

profile? – Sensitive information

• What Is Sensitive information ?– What advertizing agencies and companies want to

know– What you do not want others to find out

How can we find out private information?

• If a profile is really private how can you find out something?– What if it was not facebook? A completely

anonymous profile?– Utilize what pubic info you have.

• Using tactics that exploit friendship links• Exploiting group affiliations– Neither Facebook nor Flikr hide group

members.

BASIC model

• Guess sensitive attribute based on distribution of known attributes.

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Ana

Gia

Fabio

Emma

Chris

Bob

Don

Sensitive Info =Favorite ColorsOrangeBlueGreen

Sensitive-Attribute Inference Models

• We assume the overall distribution of the sensitive attribute is either known or it can be found using public profiles.

• We will consider the BASIC distribution to be the baseline attack.

• A successful attack is one that with extra knowledge, has significantly higher accuracy.

Our Model

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?? ??

Ana

Gia

Fabio

Emma

Chris

Bob

Don

??Bob

??

Gia

Fabio

True Blue Lovers

??

Bob

Emma Chris

Don

Espresso lovers

Sensitive Info = Favorite ColorOrangeBlueGreen

“Tell me who your friends are, and I’ll tell you who are you”

Link based Attacks• Friend-aggregate model (AGG)• Collective Classification model (CC)• Flat-link model (LINK)

Friend-aggregate model (AGG)

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?? ??

Ana

Gia

Fabio

Emma

Chris

Bob

Don

• Given my friends, what am I most likely?

• Public-Sensitive attributes/Total Links

Collective Classification model (CC)

• AGG, With re-evaluation

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?? ??

Ana

Gia

Fabio

Emma

Chris

Bob

Don

Flat-link model (LINK)

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?? ??

Ana

Gia

Fabio

Emma

Chris

Bob

Don

• Flatten the data by considering adjacency matrix of the graph.

• CLASSIFICATION!!!Ana Bob Chris Don Emma Fabio Gia color

Ana 1 1 0 1 0 0 0 ?

Bob 1 1 1 0 1 1 0 ?

Chris 0 1 1 0 0 0 0 Orange

Don 1 0 0 1 0 0 1 Green

Emma 0 1 0 0 1 1 1 Orange

Fabio 0 1 0 0 1 1 1 Blue

Gia 0 0 0 1 1 1 1 ?

Group Based Attacks

• Groupmate-link model (CLIQUE)– Considers all people in a group as

friends

• Group-based classification model (GROUP)– Considers each group as a feature in a

classifier

Groupmate-link model (CLIQUE)

??Bob

??

Gia

Fabio

??

Bob

Emma Chris

Don

Espresso loversTrue Blue Lovers

• Consider everyone in a group, a friend

• Then flatten to adjacency matrix• Use previous LINK methods after

Ana Bob Chris Don Emma Fabio Gia color

Ana 0 0 0 0 0 0 0 ?

Bob 0 1 1 1 1 1 1 ?

Chris 0 1 1 1 1 0 0 Orange

Don 0 1 1 1 1 0 0 Green

Emma 0 1 1 1 1 0 0 Orange

Fabio 0 1 0 0 0 1 1 Blue

Gia 0 1 0 0 0 1 1 ?

Group-based classification model (GROUP)

??Bob

??

Gia

Fabio

??

Bob

Emma Chris

Don

Espresso Lovers

• Use groups as a feature set– Prune away less useful groups

• Homogeneity = Entropy (h)• Size, smaller groups might be better.

True Blue Lovers

True Blue Lovers

Espresso Lovers Color

Ana 0 0 ?

Bob 1 1 ?

Chris 0 1 Orange

Don 0 1 Green

Emma 0 1 Orange

Fabio 1 0 Blue

Gia 1 0 ?

LINK-GROUP

• Use friends and groups as features and then use traditional classifierAna Bob Chri

sDon Emm

aFabio

Gia True Blue

Espresso

Color

Ana 1 1 0 1 0 0 0 0 0 ?

Bob 1 1 1 0 1 1 0 1 1 ?

Chris 0 1 1 0 0 0 0 0 1 Orange

Don 1 0 0 1 0 0 1 0 1 Green

Emma

0 1 0 0 1 1 1 0 1 Orange

Fabio 0 1 0 0 1 1 1 1 0 Blue

Gia 0 0 0 1 1 1 1 1 0 ?

Using Both –Groups and Links

• LINK-GROUP– Uses the links and groups as features in

a classifier model

Facebook Data

• Link based attacks– AGG, CC, BLOCK similar to baseline– LINK’s accuracy varied between 65.3%

and 73.5%• Group based Attacks– 73.4% success in determining gender

• Mixed-Model– 72.5%, no improvement, 57.8% or 1%

better than BASIC on political views

How good is this paper?

• How good is their attack methods?

• We can attack in more ways– Using image recognition– Using the names of people and “googling”

• Also applies to doing the same to their friends

– Search for key words in wall posts