External Defense (TTP based LBS System)

Final Year Project
(Research & Development)
External Defense
Group ID: FYP15IT-003
Presented by: Rida Qayyum Info.tec-15008
Hina Ejaz Info.tec-15010

Final Year Project
(Research & Development)
External Defense
Project Title: Provisioning Privacy for TIP attributes in Trusted
Third Party (TTP) Location Based Services (LBS) Systems
Supervised By: Dr. Muhammad Usman Ashraf

OUTLINE
 Introduction
 Problem Statement
 Research Objectives
 Literature Review
o Comparative Analysis of TTP Based
Approaches
o Critical Analysis - Selected Approach
 IJARCS Publication
 Proposed Solution
o IDP System Model
o Algorithm Design
o Framework of IDP Algorithm
 Implementation and Results
 Comparative Analysis
 Results Discussion
 Conclusion and Future work
 References
 Summary
 Q / A (Suggestions will be appreciated)

 Locations based services(LBS) System
It is a services system that provides information or making information available based on
the geographical location of the user. LBS can be used to trace the nearest cinema,
restaurant, hospital or desired destination from your location according to shortest route.
 LBS Components
o The end user's mobile device
o A mobile network to transmit data and requests for service
o The service provider's software application
o A content provider to supply the end user with geo-specific information
o A positioning component (GPS)
INTRODUCTION

LBS CATEGORIES
Location Based Services System uses three ways to provide privacy
1. Trusted Third Party(TTP) 2. Non Trusted Third Party(NTTP)
In current study, primary focus on Trusted Third Party (TTP) Location Based Services (LBS) System
3. Peer-to-Peer Networks (P2P)
LBS
user
LBS
user
(c) P2P
LBS
user
LBS
user
LBS
user

Currently, LBS attracts millions of mobile users. Extensive use of LBS raises privacy issues for the
mobile users. The main privacy issues regarding Location based services are disclosing the
o User Current Position
o His/her Personal Information
o The time of query
 In order to facilitate the mobile users with full privacy, a new privacy approach is require that will
protect Time, Identity and Position matrices for Trusted Third Party (TTP) Location Based Services
(LBS) Systems.
 Therefore, Provisioning privacy for TIP (Time, Identity, Position) attributes in Trusted Third Party
(TTP) Location Based Services (LBS) Systems
PROBLEM STATEMENT

1. To propose a new privacy approach for Trusted Third Party (TTP) Location Based
Services (LBS) Systems.
2. Protecting privacy for TIP attributes in TTP LBS System
 Protecting Time: The objective is to hide the time information when user making query to
LBS system.
 Protecting Identity: The aim is to ensure privacy to hide the user’s identity. The identity of a
user can be his/her name, a unique identiﬁer, or any set of properties uniquely identifying the
user.
 Protecting Position: The goal is to protect position of a user which he/she has send to LBS
system.
RESEARCH OBJECTIVES

LITERATURE REVIEW
Comparative Analysis of TTP based approaches

Trusted Third Party (TTP) based approaches
Techniques/
Approaches
Short Description
Privacy level
Limitation
Future Work
1 Location
Clocking
Location Cloaking uses a anonymizer and
cloaking region is created which contain the
location of a user and k-1 neighbors [34].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
Remote checking let anonymizer
update the current position of all
users, cause violation of the user
privacy.
Anonymizer needs to protect
query time of user along with his
identity and location.
2 Gruteser and
Grunwald,
k-Anonymity
This is based on the concept where a mobile user
describe an obfuscation region [35].
Identity: Yes
Spatial Info: No
Temporal Info: No
It protect identity of the LBS user but
does not provide protection against
attribute disclosure.
Protect user location and time
information along with identity.
3 Zhang et al.
strong k-
anonymity
K-anonymity can be achieved using
generalization and suppression with less
distorted results [39].
Identity: Yes
Spatial Info: No
Temporal Info: No
By using generalization and
suppression, less its computational
efficiency.
For making this heuristic-based
approaches more work is
required.
4 Bamba et al.
l-diversity
This approach assures the user location is
indistinguishable from the set of k users [40].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
It is unsatisfactory to avoid attribute
disclosure and difficult to achieve.
There is a semantic relationship
between the values of the
attribute so various levels of
privacy are required.

Techniques/
Approaches
Short Description
Privacy level
Limitation
Future Work
5 Li et al.
t-closeness
Parameter t represents the distance
between attribute disclosures within
the cluster of k users [41].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
Basically, the Earth mover's distance
(EMD) is not a perfect principle for
measuring other distance.
It may be beneficial to use both k-anonymity
and t-closeness together.
6 Domingo-Ferrer
et al. p-sensitivity
The method is to protect each user
from location attack could be de-
linked each user request form its
creator [42].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
Information loss is higher when p-
sensitive is enforced on a dataset,
according to k‐anonymity.
This approach presents a Greedy Algorithm
that protects against both identity disclosure
and attributes disclosure.
7 Mascetti et al.
historical k-
anonymity
In this technique, the system retains
track of user movement and use
information to make the anonymity
area [43].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
In historical k-anonymity Regularly
and habitually visits of user can put
his privacy in danger.
There is a need for extended research that
preserves the information of the user request.

Techniques/
Approaches
Short Description
Privacy level
Limitation
Future Work
8 Kido et al.
Position
Dummies
Dummy Position technique is used to
protect user actual position by sending
multiple "dummies" along with the true
position [44].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
It is a great challenge to create non-
distinguished dummies from the
actual user position [49].
This approach preserves privacy to user
identity and location. Time factor also
needs to protect.
9 Beresford et al.
Mix Zone
Defines areas are called mix zones, user
position is mixed with these zones [46].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
Fail to provide construction
algorithms that are effective for
mobile users moving on road
networks.
This approach preserves privacy to user
identity and location. Time factor also
needs to protect.
10 Palanisamy and
Liu, MobiMix
This technique follows the mix zone based
concept over the road network [47].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
MobiMix usually exposes
information of users, there is
unlinkability between the new and
old pseudonyms.
There is a need to consider more practical
attack models based on travel presence.

Techniques/
Approaches
Short Description
Privacy level
Limitation
Future Work
11 Policy-based
schemes
Policies are made to protect the mobile
user privacy These privacy policies are
issued by service providers [48].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
According to the selected policy, as
response service providers can hand over
the user data to others for money.
There is a need to make a more and
better policy-based scheme for
preserving user personal data.
12 Jiang et al.
Pseudonymisers
Its function is to receive user request,
send it to the service provider and
replaces the identity with the fake one
[48].
Identity: No
Spatial Info: Yes
Temporal Info: No
The main problem is that Service provider
can infer the actual identity of the user by
linking the location of the user.
There is a need to make impossible to
identify the data subject by analyzing
the related data.
13 Route Server Route Server handover the authentic
and efficient results for position queries
[4].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
The challenge was provide privacy to users
from attacker who will conclude the wrong
data in actual data [54].
In (RS) algorithm to improve privacy,
have proposed a new AES-RS
architecture.
14 AES-RS
architecture
AES-RS is based on position dummy in
which dummies generated with a single
user request [4].
Identity: Yes
Spatial Info: Yes
Temporal Info: No
AES-RS system performance enhance and
reduce after a particular time interval.
Delay variation might be possible by
the distributed approach with multiple
LBS server.

After Critical Analysis of all TTP based
approaches, we select “Position Dummy”
technique for our research objectives.

PUBLICATION IN
INTERNATIONAL JOURNAL OF ADVANCE
RESEARCH IN COMPUTER SCIENCE
(IJARCS)
Available Online at www.ijarcs.info,
Researchgate, Google Scholar.

 Actual user wants a route path to find out the nearest ATM from his current
location using over the road network.
 LBS user posted a query to location based services system in order to find out a
route path or POI (in our scenario “the nearest ATM”) from his current location.
 Location based services system is Trusted Third Party (TTP).
 When user posting query, at that time the user privacy at the risk.
 In order to overcome theses privacy issue and to protect his precise information
as current location our mechanism is to generate dummy position in a specific
area.
 The defined area can be in the form of grid or circle.
IDP SYSTEM MODEL - DESCRIPTION

 Here, the problem was by posting multiple queries several (5 – 10) times, attacker can
easily identify the actual user and can take advantage of his information
 In order to overcome this problem, whenever actual user posting query to LBS System
every time its identity will be changed.
 Basically, the identity will be changed every time LBS user posting query to LBS System.
 In LBS System, the Identity is randomly generated unique ID.
 On the basis of this mechanism, we have achieve our protection goals i.e. Time, Identity,
Position in Trusted Third Party (TTP) Location Based Services (LBS) system
 Moreover, a proper environment has been provided to the location-based services
system and the privacy issues between the user and Location Server (LS) is reduced.
IDP SYSTEM MODEL – DESCRIPTION (CONT….)

ALGORITHM DESIGN
Input: User location U(X, Y), Anonymous_Area A, Anonymity_Number K, Dummies N, π.
Output: DumArr [K(x, y) + U(X, Y)]
Procedure:
1. If (A == G (L, U)) If area is rectangular than Calculate Both Height and Width, U, L limit.
2. N ← 𝐺 Calculate Number of cells in G
3. (V, E) ∈ N Determine vertices and edges of each cell
4. Else If (A = Circle (𝜋))
5. θ =
2π
𝑘
; r =
𝐴
π
; Calculate both angle and radius
6. U(X, Y) ← Key Generator Determine Actual user generate key
7. Px ← Random (0, v(N-1))
8. Py= ← Random (0, v(N-1))

9. DumArr[Nx][Ny] Initialize 2-D array
10. i, j , x, y, N Declare variables x-axis, y-axis
11. While (i<N) Fill array with dummy positions
12. While (j<N)
13. DumArr[i][j] ←Sybil Query
14. j++;
15. end loop
16. i++;
17. end loop
18. Add Px, Py in DumArr
19. Return DumArr
ALGORITHM DESIGN (CONT.…)

By this algorithm, before sending a request to LBS system,
 Determine the Anonymity_Area A - Grid 0r Circle (line 1-5)
 Set random id provided by key generator to user location (line 6-8)
 Initialize 2-D array DumArr [Nx] [Ny] (Line 9)
 Declare variables for x-axis, y-axis x, y, Dummies N and counter variables i, j (line 10)
 Execute a nested loop to fill array with dummies N (line 11-17)
 Add user location Px, Py to array and return it (line 18-19)
Note that same procedure is repeated every time for each user posted query to TTP Based
LBS System.
PROPOSED ALROGIRTHM - DESCRIPTION

 We authenticate the performance of the proposed model with the privacy factors. For this purpose, Riverbed
Modeler academic edition 17.5 simulation tool was used. Its old name was OPNet Modeler.
 A scenario was created where the size of region A is {200m×100m}. We used Ethernet for simulation and bus
topology is constructed consisting of 30 dummy positions/nodes from multiple positions linked with each
other and it sends user’s request to LBS system for services.
SIMULATION ENVIRONMENT

SIMULATION EXPERIMENTAL RESULT
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0h 3h 6h 9h 12h 15h 18h
DataTraffic(Packets/Secs)
Duration (Hours)
Packet Send Packet Received
Data Transferring rate to LBS
We measured the data transferring rate of packet sent and received by LBS server from Ethernet.

 In our implementation, we evaluated the IDP model by conducting a comparative analysis
with existing models discussed in the literature with respect to different attributes
including
1. Data transferring rate
2. Ethernet and Wireless LAN delay
3. LBS server performance with load and query processing time
4. Route API retransmission attempts and data access rate.
These attributes with consequences have been described in the next slides.
COMPARATIVE ANALYSIS

1. MEASURING DATA TRANSMISSION RATE
Comparison on Data transferring rate to LBS

2. MEASURING DELAY
Delay in Ethernet and Wireless LAN
(a) Ethernet. Delay (sec) (b) Wireless LAN. Delay (sec)

3. MEASURING PERFORMANCE
Performance Comparison: LBS server
(a) Load (requests/secs) (b) Query Processing time (secs)

4. MEASURING DATA ACCESS DELAY & RETRANSMISSION ATTEMPTS
Route API retransmission attempts and data access rate
(b) Route API retransmission Attempts (secs)(a) Route API Data Access Delay (secs)

It was observed that IPD brought a tremendous improvement in our results as
o The success rate of packet sent and received
o Improved performance of the LBS server in terms of load and query processing time
o The delay in Ethernet and wireless WLAN is less
o Route API retransmission rate and data access rate is relatively low
However, IDP results showed that the proposed solution is more efficient than Data Dummy Array
(DDA) algorithm of AES-RS architecture based on measured parameters.
“The consequences showed that the proposed IDP model outperformed the
existing state-of-the-art privacy protection techniques by all measured attributes.”
RESULTS DISCUSSION

 LBS plays a vital role in emerging mobile computing systems. Leading to TTP based LBS systems, mobile
user is facing some substantial challenges, privacy is one of these. Fundamentally, a mobile user’s privacy is
concerned with user’s identity, spatial information and temporal information.
 This research present the absolute survey of different well-suited privacy approaches in the TTP LBS
system. The main fundamental of the conducted survey was to provide a proper environment to the LBS
system and reduce the privacy issues between the user and Location Server (LS).
 Leading to these privacy attributes, current study addressed the privacy challenge by proposing a new
privacy protection model named “Improved Dummy Position” (IDP) which is the improved version of
dummy position mechanism.
 In order to make sure the privacy authenticity, we implemented IDP in real france_highway road networks
using Riverbed Modeler academic edition 17.5 simulation tool and measured different privacy factors
including Ethernet delay, Query success rate, system performance (load and query processing time), route
API retransmission and data access rate.
 It was observe that, IDP outperformed the existing state-of-the-art models and achieved 80% privacy by
improving the rate up to 30%. However, this significant improvement provided a complete protection in all
metrics.
CONCLUSION & FUTURE WORK

 By future perspective, extend 80% privacy rate upto 100%, it is crucial to raise the user’s focus
towards the importance of location privacy and the imperilment when disclosing ones location to
the third parties. Also, it is required to test proposed model with real clients with real locations in a
real environment with a large system in order to make our contributions stronger.
CONCLUSION & FUTURE WORK

PUBLICATION IN
INTERNATIONAL JOURNAL OF ADVANCED
COMPUTER SCIENCE AND APPLICATION
(IJACSA)
Available Online at www.thesai.org,
Researchgate, Google Scholar.

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REFERENCES

 There are millions of mobile users currently using the Location-Based Services
(LBS) System. These services making information available based on the
geographical location of the user.
 But the improper use of location information put the user privacy at the risk.
Current research focuses the user privacy in the LBS system.
 The current study highlighted three attributes such as time, identity and position
to preserve user privacy in a Trusted Third Party (TTP) Location Based Services
(LBS) system. Several approaches has been studied for this purpose.
 A comparative study was conducted for critically analyze all TTP based approached
for selection of the most appropriate privacy preserving approach. Finally, we
consider “Position Dummy” technique for our research objectives.
SUMMARY

 After analysis according to our problem we propose our model, algorithm and
framework of Improved Dummy Position (IDP).
 Further, to investigate the privacy rate in the proposed solution, we quantified
different privacy attributes through simulation tool Riverbed Modeler academic
edition 17.5.
 Further, we evaluated the IDP model by conducting a comparative analysis with
existing models discussed in the literature.
 Simulation results demonstrate that our IDP could be considered as a promising
model to protect user’s TIP attributes in a TTP based LBS system due to better
performance and improved privacy level.
SUMMARY (CONT.…)

Thanks for Your Attention  

External Defense (TTP based LBS System)

External Defense (TTP based LBS System)

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