The online social network (OSN) landscape has transformed significantly over the past few years with the emergence of networks. The primary capabilities of these online networks differ. Few of the major leading ones are: Relationship networks (Facebook), Media sharing networks (Instagram), Online reviews (Zomato), Discussion forums (Quora), Social publishing platforms (Twitter), etc. In order to avail these services, users end up creating multiple identities across these platforms. For each OSN, a user defines his identity with a different set of attributes, genre of content and friends to suit the purpose of using that OSN. Researchers have proposed numerous techniques to resolve multiple such identities of a user across different platforms. However, the ability to link different identities poses a threat to the users’ privacy; users may or may not want their identities to be linkable across networks. In this study, we model the notion of linkability as the probability of an adversary (who is part of the user’s network) being able to link two profiles across different platforms, to the same real user. The major factors that lead to increased linkability across social networks are similar profile attributes and cross posting across the social networks. To make users aware of the linkability across multiple social networks, as part of the thesis, we have developed a framework, which assists the users to control their linkability. It has two components; a linkability calculator that uses three state-of-the-art identity resolution techniques to compute a normalized linkability measure for each pair of social network platforms used by a user, and a soft paternalistic nudge. The user configures the desired linkability score range for each pair of networks. There are two types of nudge: Attribute-driven Notification Nudge, which alerts the user through a pop-up notification if any of their activity violates their preferred linkability score range and Content-driven Color Nudge, which notifies the user by changing the color of the box bounding the post update from black to red if the content being posted by them is found to be similar to the content already posted by them on a different social network. We evaluate the effectiveness of the nudge by conducting a controlled user study on privacy conscious users who maintain their accounts on Facebook, Twitter, and Instagram. Outcomes of user study confirmed that the proposed framework helped 75% of participants to take informed decisions, thereby preventing inadvertent exposure of their personal information across social network services. Also, the content driven color nudge refrained few participants from making post updates.
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User Identities Across Social Networks: Quantifying Linkability and Nudging Users to control Linkability
1. User Identities across Social Networks:
Quantifying Linkability and Nudging Users to control Linkability
Dr.
Ponnurangam
Kumaraguru
(Advisor)Srishti
Chandok
2. Thesis Committee
Dr.
Arun
Balaji
Buduru,
IIIT-‐Delhi
Dr.
Anuja
Arora,
JIIT-‐Noida
Dr.
Ponnurangam
Kumaraguru
(Chair),
IIIT-‐Delhi
2
4. Why people join multiple OSNs???
Type of content being shared:
✦ Images
✦ Videos
✦ Short messages
✦ Combination of messages, video and images
✦ Online reviews
✦ Discussion forums
Type of network being offered:
✦ Professional network
✦ Personal network
4
5. Notion of Linkability
Linkability
is
a
metric
which
quantifies
closeness
between
two
identities
belonging
to
the
same
user
on
different
social
networks
5
Username: RaineRamirez1
Name: Chris Raine Ramirez
Location: Caloocan City
Website: NULL
Username: Rainevouz
Name: Christopher Delgar Ramirez
Bakunawa
Location: San Jose del Monte,
Bulacan
Website: NULL
0.31Linkability Score =
There is a 31% chance that
Rainevouz & RaineRamirez1
is the same person
6. Motivation
6
✦ Social audience = 437,632 + 153,000 + 805,097 or less??
✦ Targeted Marketing using aggregated data
De-duplicating audience - finding linkability across OSNs
9. 9
1. Hacker studies Karla
who is active on
2. Information aggregation:
• Likes playing guitar
and softball
• Works on making
reports at the office
and recently received
an award
• Likes wine
• Nick is her boss and
Curt is her colleague
3. Hacker crafts an email
4. Downloaded on
Karla’s machine
5. Hacker installs a
remote access tool
on the machine
10. Primary School
name on
10
Motivation
Streets close
to ‘xyz’ school
‘xyz’ school
Security Question:
Street where you grew up?
Cracking passwords - Personal data across multiple social networks to
gather answers for password recovery questions
11. Research Aim
“
Develop
a
real-‐time
system
which
can
help
users
to
maintain
their
linkability
across
social
networks.
”
AIM
Linkability
Score
Computation
Linkability
Nudge
Design
11
12. Current State of Art
Identity
Resolution
12
Privacy
Nudges
Linkability Score Linkability Nudge
16. Privacy Nudges (Contd..)
Privacy Nudges:
✦Wang, Yang, et al. Privacy nudges for social media: an exploratory
Facebook study - Profile Picture nudge, timer nudge and sentiment
nudge
16
17. 17
✦Ronald. Context is everything sociality and privacy in online social
network sites - Segregation of audience for profile attributes of user on
OSNs so that its visibility is controllable.
Privacy Nudges (Contd..)
18. Novelty
18
‹#›
Nudging users to prevent disclosures
owing to the resolution of their multiple
identities
Identity
Resolution
Privacy
Nudges
24. Data Collection (Contd..)
24
Streaming
API
Tweets
Fb.me
links
Link
Expander
Database
http://fb.me/8dR49RHpQ
https://www.facebook.com/
christie.andresen/posts/
10210420711856356
25. Description of Data Count
Positive Data 23,985
Negative Data (Type I) 96,130
Negative Data (Type II) 24,560
Positive Data: <IFb> = <ITw>, identities are same [bob12, bob12]
Negative Data (Type I): <IFb> ≠ <ITw> but the identities appear to be similar [bob_c, bob_d]
Negative Data (Type II):> <IFb> ≠ <ITw> and the identities appear to be dissimilar [bob, alice]
Data Collection (Contd..)
25
27. Weighted Sum Method
Username
Name
Location
Website
Longest
Common
Subsequence,
Edit Distance,
etc
Weighted
Sum
Calculator
0.31
27
Username = 0.39
Name = 0.28
Location = 0.6
Website = 0
Username: RaineRamirez1
Name: Chris Raine Ramirez
Location: Caloocan City
Website: NULL
Username: Rainevouz
Name: Christopher Delgar Ramirez
Bakunawa
Location: San Jose del Monte,
Bulacan
Website: NULL
Feature
Extractor
Metric
Calculator
Linkability
Score
Linkability = ∑wi * fi
Score
∑wi
28. Need for different Weights
Feature weights = 1,1,1,1 for username, name,
geo-location and website/url, respectively.
Feature weights = 2,3,4,1 for username, name,
geo-location and website/url, respectively.
To increase the difference between positive data and negative data thereby, ensuring
that negative data (Type I) identity would not be mistaken to be as positive identity.
28
31. Comparison of Baseline methods
Accuracy is 87% when threshold value = 0.39
[feature weights used are 2, 3, 4 and 1 for
username, name of user, location and website
features, respectively]
Accuracy is 32% when threshold value = 0.71
31
32. Limitations of Baseline Methods
Probabilistic
method
did
not
produce
anticipated
results
with
quiet
low
accuracy.
Both
the
methods
employ
a
small
set
of
features
namely
name,
username,
geo-‐location
and
website.
They
fail
to
capture
user’s
content
sharing
behavior.
32
33. Take-aways from Baseline Methods
Weighted
Sum
method
performed
better
than
Probabilistic
method.
We
will
enhance
the
feature
set
using
well
known
identity
resolution
techniques
+
our
proposed
Weighted
Sum
method
to
compute
linkability
scores
33
43. 1. Browser Extension
Maintains user's identity across the entire user session.
Captures user's posting activity and changes in profile attributes on all
configured OSM platforms.
Displays linkability nudge in various forms (notifications and color).
43
Downloads the
Chrome
browser extension
Nudge
Server
Send User’s Activity
Information
Linkability Score
and
Piecharts
Browser
Extension
44. 2. Nudge Server
Intermediary between the browser extension and linkability compute server.
Receives user's access token from browser extension and sends them to
linkability compute server to obtain user's data.
Passes the information pertaining to user's activities like making a post or
changing profile attribute to the linkability compute server.
Sends across the newly computed linkability scores to the browser extension
from time to time based upon user's activities.
44
Access Token for
various OSNs
Forward Access Token
for various OSNs
Linkability Score Forward User’s
Activity Information
Nudge
Server
Linkability
Compute
Server
Browser
Extension
45. 3. Linkability Compute Server
Fetches user's data from the API endpoints.
Implements the identity resolution methods to compute linkability scores.
Receives every user’s activity information (post or profile attribute),
recomputes linkability scores and sends them back to nudge server.
45
Nudge
Server
Linkability
Compute
Server
Identity Resolution
Algorithms
NEMO | HYDRA | MOBIUS
Fetch user’s data
Linkability Score
Linkability Score
46. Nudge Design
Content-driven Color Nudge -
✦ Similar Post --> Red color nudge
✦ Dissimilar Post --> Green color nudge
Attribute-‐driven
Notification
Nudge
-‐
✦ Profile attribute update -> Linkability Score crosses range ->
Notification Popup nudge
46
50. User Evaluation of the Nudge
50
Control
Period
Treatment
Period
No exposure to
linkability nudge
Tasks (Post and
profile updates)
Exposure to
linkability nudge
Tasks (Post and
profile updates)
51. Analysis of User Evaluation
58%
of
the
participants:
Understood
the
broad
concept
of
linkability
score
42%
of
participants:
More
aware
about
the
linkability
of
their
multiple
identities
across
OSNs
84%
of
the
participants:
Noticed
the
factors
contributing
to
their
linkability
scores
83%
of
the
participants:
Liked
Color
nudge
and
pie-‐charts
more
Activities
performed
by
one
of
the
participants 51
52. Limitations
Time
delay
(2-‐5
seconds)
while
making
post
during
treatment
period.
Used
uniform
weights
for
computing
linkability
scores.
System
works
for
three
social
networks.
Evaluated
the
nudge
with
a
small
number
of
participants.
52
53. Conclusions
Leverage
features
from
well
known
methods
for
identity
resolution
(NEM,
HYDRA
and
MOBIUS)
and
use
the
proposed
baseline
method,
Weighted
Sum,
to
compute
the
linkability
scores
Identify
the
factors
(profile
attributes
and
content)
that
have
contributed
to
the
computed
linkability
score
Design
and
develop
linkability
nudge,
a
soft
intervention
which
alerts
users
whenever
user
behavior
leads
to
change
in
linkability
score
beyond
preconfigured
range
Perform
a
detailed
user
study
in
a
controlled
lab
experiment
setting
to
assess
effectiveness
and
utility
of
proposed
linkability
nudge
53
58. Feature Name Metrics
username Hamming Distance, Longest Common Subsequence, Edit Distance,
Cosine Distance, Jaccard Distance, Jaro Winkler Distance
name Length of Common Substring, Length of Common Prefix & Common
Suffix
location Length of Common Substring, Geo-location (LAtitude & Longitude)
website Canonical URL matching
Features and Metrics for Baseline Methods
58