Privacy preserving optimal meeting location determination on mobile devices

Privacy-Preserving Optimal Meeting Location
Determination on Mobile Devices
Abstract:
Equipped with state-of-the-art smart phones and mobile devices, today’s
highly interconnected urban population is increasingly dependent on these gadgets
to organize and plan their daily lives. These applications often rely on current(or
preferred) locations of individual users or a group of users to provide the desired
service, which jeopardizes their privacy; users do not necessarily want to reveal
their current (or preferred)locations to the service provider or to other, possibly un-
trusted, users. In this paper, we propose privacy-preserving algorithms for
determining an optimal meeting location for a group of users. We perform a
thorough privacy evaluation by formally quantifying privacy-loss of the proposed
approaches. In order to study the performance of our algorithms in a real
deployment, we implement and test their execution efficiency on Nokia smart
phones. By means of a targeted user-study, we attempt to get an insight into the
privacy-awareness of users in location based services and the usability of the
proposed solutions.
We develops Computer Engineering Projects for BE/ME students. For any kind of support you may live
chat with us at www.ocularsystems.in or call us on 020 30858066 or
Mail Us: info@ocularsystems.in
Our Address: Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46 (Maharashtra)

Architecture Diagram:
Existing System:
The rapid proliferation of smart phone technology in urban communities has
enabled mobile users to utilize context aware services on their devices. Service
providers take advantage of this dynamic and ever-growing technology landscape
by proposing innovative context-dependent services for mobile subscribers.
Location-based Services (LBS), for example, are used by millions of mobile

subscribers every day to obtain location-specific information .Two popular features
of location-based services are location check-ins and location sharing. By
checking into a location, users can share their current location with family and
friends or obtain location-specific services from third-party providers ,The
obtained service does not depend on the locations of other users. The other type of
location-based services, which rely on sharing of locations (or location
preferences) by a group of users in order to obtain some service for the whole
group, are also becoming popular. According to a recent study , location sharing
services are used by almost 20% of all mobile phone users. One prominent
example of such a service is the taxi-sharing application, offered by a global
telecom operator , where smart phone users can share a taxi with other users at a
suitable location by revealing their departure and destination locations. Similarly,
another popular service enables a group of users to find the most geographically
convenient place to meet.
Disadvantages:
1.Privacy of a user’s location or location preferences, with respect to other users
and the third-party service provider, is a critical concern in such location-sharing-
based applications. For instance, such information can be used to de-anonymize
users and their availabilities , to track their preferences or to identify their social
networks. For example, in the taxi-sharing application, a curious third-party service
provider could easily deduce home/work location pairs of users who regularly use
their service.

2.Without effective protection, evens parse location information has been shown to
provide reliable information about a users’ private sphere, which could have severe
consequences on the users’ social, financial and private life . Even service
providers who legitimately track users’ location information in order to improve
the offered service can inadvertently harm users’ privacy, if the collected data is
leaked in an unauthorized fashion or improperly shared with corporate partners.
Proposed System:
We then propose two algorithms for solving the above formulation of the FRVP
problem in a privacy-preserving fashion, where each user participates by providing
only a single location preference to the FRVP solver or the service provider.
In this significantly extended version of our earlier conference paper ,we evaluate
the security of our proposal under various passive and active adversarial scenarios,
including collusion. We also provide an accurate and detailed analysis of the
privacy properties of our proposal and show that our algorithms do not provide
any probabilistic advantage to a passive adversary in correctly guessing the
preferred location of any participant. In addition to the theoretical analysis, we also
evaluate the practical efficiency and performance of the proposed algorithms by
means of a prototype implementation on a test bed of Nokia mobile devices. We
also address the multi-preference case, where each user may have multiple
prioritized location preferences. We highlight the main differences, in terms of
performance, with the single preference case, and also present initial experimental
results for the multi-preference implementation. Finally, by means of a targeted
user study, we provide insight into the usability of our proposed solutions.

Advantages:
We address the privacy issue in LSBSs by focusing on a specific problem called
the Fair Rendez-Vous Point (FRVP) problem. Given a set of user location
preferences, the FRVP problem is to determine a location among the proposed
ones such that the maximum distance between this location and all other users’
locations is minimized, i.e. it is fair to all users.
Goal:
Our goal is to provide practical privacy preserving techniques to solve the FRVP
problem, such that neither a third-party, nor participating users, can learn other
users’ locations; participating users only learn the optimal location. The privacy
issue in the FRVP problem is representative of the relevant privacy threats in
LSBSs.
Algorithms:
Our proposed algorithms take advantage of the homomorphic properties of well-
known cryptosystems, such as BGN, ElGamal and Paillier, in order to privately
compute an optimally fair rendez-vous point from a set of user location
preferences.

Implementation Modules:
1. User Privacy
2. Server Privacy
3. PPFRVP protocol
4. Privacy Under Multiple Dependent Executions
User Privacy:
The user-privacy of any PPFRVP algorithm A measures the probabilistic
advantage that an adversary a gains towards learning the preferred location of at
least one other user ,except the final fair rendez-vous location, after all users have
participated in the execution of the PPFRVP protocol. An adversary in this case is
a user participating in A. We express user-privacy as three different probabilistic
advantages.
1. we measure the probabilistic advantage of an adversary ua in correctly
guessing the preferred location Li of any user ui _= ua. This is referred to as
the identifiability advantage.
2. The second measure of user-privacy is the distance linkability advantage,
which is the probabilistic advantage of an adversary ua in correctly guessing
whether the distanced i, j between any two participating users ui _= u j , is

greater than a given parameter s, without learning any users’ preferred
locations Li , L j.
3. The coordinate-linkability advantage, denoted as Advc−LNKa , is the
probabilistic advantage of an adversary ua in correctly guessing whether a
given coordinate xi (or yi )of a user ui is greater than the corresponding
coordinate(s)of another user u j _= ui without learning the users’ preferred
locations Li , L j .
Server Privacy:
For the third-party (LDS) adversary, the game definitions are similar to those
defined for an user adversary, except that the LDS does not receive L f air in the
Step 2 of the game. Then, the server-privacy of a PPFRVP algorithm A can then
be defined as follows. Definition 3: An execution of the PPFRVP algorithm A is
server-private if the identifiability advantage DTLDS(A), the distance-linkability
advantage Advd−LNKLDS and the coordinate linkability advantage
Advc−LNKLDS of an LDS are negligible. In practice, users will execute the
PPFRVP protocol multiple times with either similar or completely different sets of
participating users, and with the same or a different location preference in each
execution instant. Thus, although it is critical to measure the privacy leakage of the
PPFRVP algorithm in a single execution, it is also important to study the leakage

that may occur over multiple correlated executions, which in turn depends on the
intermediate and final output of the PPFRVP algorithm. We discuss the privacy
leakage of the proposed algorithms over multiple executions in Section VI-D.
PPFRVP protocol:
The PPFRVP protocol (shown in Fig. 4) has three main modules:
(A) the distance computation module,
(B) the MAX module and
1) Distance Computation: The distance computation module uses either the BGN-
distance or the Paillier- ElGamal distance protocols. We note that modules (B) and
(C) use the same encryption scheme as the one used in module (A). In other words,
(E).It refers to encryption using either the BGN or the Paillier encryption scheme.
2) MAX Computation: In Step B.1, the LDS needs to hide the values within the
encrypted elements (i.e., the pair wise distances computed earlier) before sending
them to the users.
This is done in order to
(i) ensure privacy of real pair wise distances,
(ii) be resilient in case of collusion among users and
(iii) preserve the internal order (the inequalities) among the pair wise distance
from each user to all other users.
Privacy Under Multiple Dependent Executions:

As defined earlier, in a dependent execution of the PPFRVP protocol, all the
involved parties possess information from the previous executions, in addition to
the current input, output and intermediate data. It is clear that, due to the oblivious
or blind nature of the computations, the privacy guarantees of the proposed
PPFRVP protocols with respect to the LDS independent executions remains the
same as that for independent executions. Furthermore, dependent executions in
which the information across executions is completely uncorrelated (e.g., different
set of users in each execution or different and unrelated preferences in each
execution) reduce to independent execution. We analyze two different scenarios of
dependent
executions involving differential information .First, we consider the case of
dependent executions with different subsets of participants. We assume that, in
each sequential execution, the set of users or participants is reduced by exactly one
(the adversary participant remains until the end), and that the retained participants
preferences remain the same as the previous execution(s). The following
information is implicitly passed across executions in this scenario:
(i) participant set,
(ii) optimal fair location L f air ,
(iii) permuted and randomly scaled pair wise distances from
the participant to every other participant, and (iv) scaled (but order preserving)
maximum distance from every participant to every other participant.

System Configuration:-
H/W System Configuration:-
Processor - Pentium –III
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
S/W System Configuration:-
 Operating System :Windows95/98/2000/XP
 Front End : java, jdk1.6
 Database : My sqlserver 2005
 Database Connectivity : JDBC.

Privacy preserving optimal meeting location determination on mobile devices

More Related Content

What's hot (18)

Similar to Privacy preserving optimal meeting location determination on mobile devices (20)

More from Adz91 Digital Ads Pvt Ltd (20)

Recently uploaded (20)

Privacy preserving optimal meeting location determination on mobile devices