This document discusses benchmarking privacy-preserving people search. Social match is important in people search because tighter social similarity makes it easier to connect people. However, privacy is a big concern with people search using social networks due to users opting out of sharing information or providing incomplete data. The research aims to obtain a privacy-preserving social network by simulating with public coauthor networks. Both global and local network features are important for people search performance, and accounting for privacy concerns in these features can significantly impact search results. The local network feature, relating to direct connections, has more influence than global features relating to whole network propagation.

1. Benchmarking
the
Privacy-­‐Preserving
People
Search
Shuguang Han, Daqing He and Zhen Yue
SIGIR 2014 Workshop on Privacy-Preserved Information Retrieval

2. Social
Match
is
Important
in
People
Search
2
because a tighter social similarity
make it easier for people to
connect
Then
Need the users’ social networks
to return the potential candidates
who have either direct or indirect
connections with the given
users.

3. But
Privacy
is
a
BIG
Concern
in
People
Search
3
— People
search
often
performed
in
many
users
in
SNS
such
as
Facebook,
LinkedIn
— Users
in
SNS
often
either
opt
out
from
certain
social
networks
or
provide
incomplete
or
even
fake
information.
— data
mining
algorithms
may
not
work
or
even
harm
the
user
experience
when
equipped
with
such
incomplete
and
noisy
social
information

4. People
Search
Studies
o:en
Adopt
Public
Available
Network
4
Coauthor
networks
are
often
used
because
of
lacking
privacy
concerns
However
this
limits
the
type
of
people
search
being
studied,
So
should
study
other
social
networks
which
has
privacy
concerns

5. Our
Research
— Obtaining
a
privacy-­‐preserving
social
network
— By
simulating
with
the
public
available
coauthor
networks
— Assume
coauthor
networks
could
be
used
as
surrogates
— Motivation1:
many
real-­‐world
social
networks
(including
coauthor
networks
and
many
other
privacy-­‐concerned
networks
such
as
Facebook
social
networks)
share
the
same
patterns
[Ugander,
et
al
2011,
Barabási
et
al.
1999,
Watts,
et
al
1998]
— All
small-­‐world
networks
and
their
degree
distributions
are
highly
skewed.
— Motivation
2:
assortative
patterns
(the
preferences
of
connecting
people
who
share
the
similar
features)
of
social
networks
are
all
assortatively
mixed
[Newman
2002]
— whereas
the
technological
and
biological
seems
to
be
disassortative.
5

6. Research
Focuses
— Privacy
issues
on
people
search
— Either
global
and/or
local
network
features
used
in
people
search
— Global
network
features:
the
features
that
are
propagated
through
the
whole
networks
— measured
by
the
PageRank
value
running
on
the
whole
social
networks
— Local
network
features:
the
features
that
are
directly
related
to
the
ego-­‐network
of
the
querying
user
— measured
by
the
proportion
of
common
social
connections
— Query
users’
privacy
concerns
and
candidates’
privacy
concerns
6

7. Data
— Academic
publication
collection
— containing
219,677
conference
papers
from
the
ACM
Digital
Library.
— between
1990
and
2013.
— Only
public
available
information
of
a
paper:
the
title,
abstract
and
authors
— No
further
author
disambiugation
besides
ACM
Digital
Library
author
ID
— In
total,
the
collection
contains
253,390
unique
authors
and
953,685
coauthor
connection
instances.
— Users’
people
search
activities:
Han
et
al.
[5]
evaluation
of
a
people
search
system.
— four
different
people
search
tasks,
each
aimed
to
search
for
5
candidates.
— A
baseline
plain
content-­‐based
people
search
system
— An
experimental
system
that
enhances
people
search
with
three
interactive
facets:
content
relevance,
social
similarity
between
the
user
and
a
candidate
(the
local
network
feature)
and
the
authority
of
a
candidate
(the
global
network
feature).
— The
experiment
system
allowed
the
querying
users
to
tune
the
value
associated
with
each
facet
in
order
to
generate
a
better
candidate
search
results.
— 24
participants
were
recruited
for
that
user
study.
— At
the
beginning
of
the
user
study,
each
participant
was
asked
to
provide
their
publications
and
their
close
social
connections
(such
as
advisors).
— In
the
post-­‐task
questionnaire,
the
participants
were
asked
to
rate
the
relevance
of
each
marked
candidate
in
a
Five-­‐point
Likert
scale
(1
as
non-­‐relevant
and
5
as
the
highly
relevant).
7

8. SimulaDng
Privacy-­‐Concern
networks
8
pi is the probability of a
given user has privacy
concern, di is the degree of
association of a user and
dmax. Is the maximized
degree in the network, λ
helps to establish different
selection strategies
Evaluation:
• How the new network impact people search performance: MAP

9. Impacts
of
Global
Network
Feature
of
Privacy-­‐
Concern
Network
on
People
Search
9

10. Impacts
of
Local
Network
Feature
of
Privacy-­‐Concern
Network
on
People
Search
10

11. Impacts
of
Query
Users’
Privacy
Concerns
on
People
Search
11

12. Insights
— Both
the
local
and
global
network
features
are
important
for
the
performance
of
people
search
(compare
to
not
using
social
network).
— Comparing
to
the
global
network
feature,
the
local
network
feature
is
more
important.
— Privacy-­‐concerns
reflected
in
local
and
global
network
features
can
significantly
influences
on
the
performance
of
people
search
— The
privacy
concerns
from
the
high-­‐degree
candidates
in
the
network
will
have
more
impacts
on
global
features.
— The
local
network
feature
is
related
to
both
the
querying
users
and
the
candidates
in
the
networks.
— the
privacy
concerns
from
both
of
them
have
significant
impact
on
the
people
search
performance.
— The
privacy
concerns
from
high-­‐degree
candidates
have
bigger
influences
on
the
people
search
than
that
of
the
lower-­‐degree
candidates,
especially
when
those
high-­‐degree
candidates
are
related
to
the
querying
user.
— We
also
find
that
if
the
querying
users
provide
more
social
connections,
the
search
performance
would
increase
steadily.
12