LOCATION BASED
SOCIAL NETWORKING
CHALLENGES
AND SOLUTIONS
AYESHA BEGUM
MOUNIKA KOLLURI
SRAVANI DHANEKULA
OUTLINE
 INTRODUCTION
 EXAMPLES OF LBSN APPLICATIONS
 PROS AND CONS OF LBSN
 MOTIVATION
 SELECTED PAPERS
 MEASURING USER ACTIVITY ON AN ONLINE LOCATION BASED
SOCIAL NETWORK.
 PLACE RECOMMENDATION FROM CHECK-IN SPOTS ON LBSN.
 LOCATION CHEATING ON LBSN.
 COMPARISON & ANALYSIS
 CONCLUSION
 REFERENCES
INTRODUCTION:
 LBSN is a location based service that utilizes location
information to facilitate social networking.
 LBSN is the convergence between location based services (LBS)
and online social networking (OSN).
 LBSN applications offer users the ability to look up the location
of another “friend” remotely using a smart phone, desktop or
other device, anytime and anywhere.
They allow users to check-in at places and share their location
with friends, thereby providing a new facet of user online
behavior.
EXAMPLES OF LBSN APPLICATIONS
PROS &CONS OF LBSN
PROS:
It is the best means to share what we are doing and where we are at.
Nearby, locations like restaurants, parks, zoo's can be easily found out.
CONS:
LBSN lacks privacy of an individual and exposes user’s information.
MOTIVATION
A measurement study of user activity is needed in order to know
how users connect with friends and also how they check- in at
different places on online location-based social network with
hundreds of thousands of users in it.
People generally don’t know the interesting locations when they go
to new place. So, the social networking sites must be able to
recommend places to them based on their area of interest gathered
from previous check-ins.
 Location-based social network services must be able to keep track
of the users who cheat on their location information.
MEASURING USER ACTIVITY ON AN ONLINE
LOCATION-BASED SOCIAL NETWORK
The main aim of the paper is to present a measurement study of
user activity on a popular Online Location Based Social Network.
The paper mainly investigates user activity by analyzing not only
the number of friends user has, but also the number of check-ins
made and the places visited.
EXISTING AND PROPOSED APPROACH
Existing Approach:
User Activity measurement is focused mainly on number of friends
user has.
Proposed Approach:
User activity is measured based on three factors
 Adding Online Friends
 Making Check-ins
 Visiting new places
METHODOLOGY PROPOSED
Complete data set of users is collected through a public API.
 User activity is measured, based on adding online friends, making
check-ins and visiting new places. Probability distribution of these
factors with respect to a user are plotted individually.
 Based on the dates of both the earliest and the latest check-in that a user
has made, account age and activity span of the user are estimated.
The user activity age and user account age are plotted graphically based
on Complementary Cumulative Distribution Function (CCDF).
EXPERIMENTAL ANALYSIS
From the graphical analysis done in the paper, it states that
 It appears easier and quicker to accumulate friends than to
accumulate new places and check-ins.
 It has been derived that an account which has been active for a
longer period is more likely to accumulate more friends, checkins and places than an account only active for a shorter amount
of time.
 User account life span decays faster than exponentially.
PLACE RECOMMENDATION FROM CHECK- IN
SPOTS ON LBSN
The paper mainly discusses about a user-based collaborative
filtering method to make a set of recommended places for a
user, in which similarity of users is calculated and similar
users’ records are used to predict places the user likes.
In addition to this, similarity of users check-in activities is
calculated not only on their positions but on their semantics
like shopping, eating, drinking, etc.
EXISTING APPROACH
In Collaborative Filtering Based Approach, an item is
recommended to a user based on past information of the people
with similar tastes and preference.
In Personalized Recommender Approach ,check-in information is
crawled to generate a user/spot rating matrix. By predicting the
interest of users in certain spots, this technique recommends places
users have not been visited previously.
PROPOSED APPROACH
All of the existing methods do not consider the issue of the
semantics of GPS location.
In the proposed method
 Firstly the names of the noted places are attached to GPS location
data and hierarchical category graph framework is built.
 Recommendation of places is done by applying a typical CF
approach that was not applied previously
DESCRIPTION ABOUT CHECK IN RECORDS
SINA microblog is used as a data source to collect user’s check in
spots.
.
METHODOLOGY
Density-based clustering method cluster’s all of users’ check in spots
into several regions.
In the next step, for each cluster, the gravity center of member’s position
is calculated itto represent the position of the cluster .
 Each cluster is annotated by using POIs database. Then a semantic
hierarchical category-graph framework is applied to analyze users’
interests and similarity score between clusters.
Top-N similar users are selected and the users’ records are used for
user-based collaborative filtering. Based on this, some unvisited places
are recommended.
FIGURE SHOWING SEMANTIC HIERARCHICAL
FRAMEWORK
EXPERIMENTS & ANALYSIS
SINA microblog, is used , among them, 268 users who checked in
more than 25 times are selected.
In the clustering analysis ,the neighborhood radius ε actually equals
to distance50 meters,
After the clustering, Foursquare database is used as POIs data to
annotate clusters.
EXPERIMENTAL RESULTS WITH VARIOUS
RECOMMENDER PLACES FROM DIFFERENT TOPN SIMILAR USERS.
LOCATION CHEATING: A SECURITY CHALLENGE
TO LOCATION-BASED SOCIAL NETWORK
SERVICES
The paper mainly discusses about the location-based mobile social
network's which generally attract more no of user's, in order to
provide real-world rewards to the user, when a user checks in at a
certain venue or location.
This gives incentives for the cheaters to cheat on their locations.
Reasons for Location cheating
 Lack of proper location verification mechanisms.
 Loosely regulated anti cheating rules.
PROPOSED METHODOLOGY
Firstly, the threat of location cheating attacks is identified.
Secondly, the root cause of the vulnerability is found out,
and possible defending mechanisms are outlined.
Foursquare is used as an example to introduce a novel
location cheating attack. In addition to this, the foursquare
website is crawled.
The crawled data is analyzed, in order to prove that the
automated large scale cheating is possible.
CHEATER CODE
It is used to defend against the location cheating attacks.
 Function: It is to verify the location of a device by using the GPS
function of that device.
When a user claims that he/she is currently in a location far away
from the location reported by the GPS of his/her phone, the checkin will be considered invalid and won’t yield any rewards.
CRITERIA FOR CHEATER CODE
Criteria used in determining location cheating in the cheater code is
as follows
 Frequent check-ins: This rule prevents a user from checking in
frequently to get as many points as possible
 Super human speed: This rule limits location cheating by a
single user to a small geographic area.
 Rapid-fire check-ins: This rule stops a user from checking into
multiple venues in a small area and within a short time period.
DIFFERENT LEVELS OF CHEATING ATTACK:
Location Cheating Against G.P.S Verification:
An attacker blocks the information provided by the GPS and feeds
fake location information to the LBS application, thereby making the
server believe that it is in fake location.
Crawling Data: By changing the ID in the URL, almost all of the
user information and venue profiles is crawled. This is a serious
security weakness and should be patched soon.
ILLUSTRATION OF LOCATION CHEATING
DIFFERENT LEVELS OF CHEATING ATTACK
Automated Cheating:
 Location coordinates of victim venues are found out by
computer program.
 List of venues that need to be checked-in are selected
automatically by analyzing the cheater code.
Cheating With Venue Profile Analysis
 Location cheaters gain intelligence from the venue analysis
after the crawling.
EXPERIMENT ANALYSIS OF LOCATION
CHEATING ON FOUR SQUARE
Above Normal Level of Activity: High ratio of recent check-ins to
total check-ins of a user indicates that it is likely a user plays tricks
to stay in the recent visits list, which is a sign of cheating.
Below Normal level of Rewards: User having a large amount of
check-ins but little rewards indicates that user is detected as a
cheater.
 Suspicious Check-in Patterns: Check-in pattern or history is
examined to tell if a user is a location cheater through further
analysis of the crawled data.
POSSIBLE SOLUTIONS BASED ON EXPERIMENTS
Location Verification Techniques:
 Address Mapping
 Venue Side Location Verification
Mitigating Threat from Location Cheating
 Access control for Crawling
 Hiding information from profiles
COMPARISON AND ANALYSIS
Paper 1
Paper 2
Paper 3
Techniques focused on how the
user activity can be effectively
measured
Techniques discussed for
extracting the check-in spots
based on user’s interest
Techniques for enhancing the
security of local
information
Experiments performed on
Gowalla LBSN site
Experiments performed on SINA Experiments performed on
LBSN site
Foursquare LBSN site
It highlighted the differences in
the distribution of friends,
check-ins and places
It recommends unvisited places
by analyzing user’s interest
It provides better solutions to
identify possible cheaters.
CONCLUSIONS
All the three papers discussed on location-based services which
utilize the geographical position to enrich user experiences in a
variety of contexts.
The papers conclude that Location based Features can effectively
measure user activity, recommend unvisited places and also detect
threat of location cheating attacks.
REFERENCES:
 Scellato, S.; Mascolo, C. Measuring user activity on an online location-basedsocial network Computer
Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on Topic(s): Communication,
Networking & Broadcasting ;Components, Circuits, Devices & Systems ; Computing & Processing
(Hardware/Software) ; Engineering Profession ;General Topics for Engineers (Math, Science &
Engineering) ;Signal Processing & Analysis Digital Object Identifier: 10.1109/INFCOMW.2011.5928943
Publication Year: 2011 , Page(s): 918 - 923 Cited by 1
 Hongbo, Chen; Zhiming, Chen; Arefin, Mohammad Shamsul; Morimoto, Yasuhiko Place
Recommendation from Check-in Spots onLocation-Based Online Social Networks Networking and
Computing (ICNC), 2012 Third International Conference on Topic(s): Communication, Networking &
Broadcasting ;Components, Circuits, Devices & Systems ; Computing & Processing (Hardware/Software)
Digital Object Identifier: 10.1109/ICNC.2012.29 Publication Year: 2012 , Page(s): 143 – 148
 Wenbo He; Xue Liu; Mai Ren Location Cheating: A Security Challenge toLocation-Based Social Network
Services Distributed Computing Systems (ICDCS), 2011 31st International Conference on Topic(s):
Communication, Networking & Broadcasting ;Computing & Processing (Hardware/Software) Digital
Object Identifier: 10.1109/ICDCS.2011.42 Publication Year: 2011 , Page(s): 740 - 749 Cited by 2
Thank You