IEEE 2014 JAVA MOBILE COMPUTING PROJECT Privacy-Preserving Optimal Meeting Location Determination on Mobile Devices

7/21/2019 IEEE 2014 JAVA MOBILE COMPUTING PROJECT Privacy-Preserving Optimal Meeting Location Determination on Mobil

1/10

Privacy-Preserving Optimal Meeting LocationDetermination on Mobile Devices

Abstract:

Equipped with state-of-the-art smart phones and mobile devices, todays

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 !eopardizes their privacy" users do not necessarily want to reveal

their current (or preferredlocations to the service provider or to other, possibly un-

trusted, users. #n this paper, we propose privacy-preserving algorithms for

determining an optimal meeting location for a group of users. $e perform a

thorough privacy evaluation by formally quantifying privacy-loss of the proposed

approaches. #n order to study the performance of our algorithms in a real

deployment, we implement and test their e%ecution efficiency on &o'ia smart

phones. y means of a targeted user-study, we attempt to get an insight into the

GLOBALSOFT TECHNOLOGIESIEEE PROJECTS & SOFTWARE DEVELOPMENTS

IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE

BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS

CELL: +91 9!9" #9$"% +91 99' #"(% +91 9!9" "(9$% +91 9($1! !$!$1

V)*): ,,,-.)02304546738*-64 M0) 6:)333.)0*3546738*;0)-86
mailto:[email protected]:[email protected]:[email protected]


2/10

privacy-awareness of users in location based services and the usability of the

proposed solutions.

Architecture Diagram:

Existing System:


3/10

The rapid proliferation of smart phone technology in urban communities has

enabled mobile users to utilize conte%t aware services on their devices. )ervice

providers ta'e advantage of this dynamic and ever-growing technology landscape

by proposing innovative conte%t-dependent services for mobile subscribers.

*ocation-based )ervices (*), for e%ample, 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. y

chec'ing 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. +ccording to a recent study , location sharing

services are used by almost of all mobile phone users. /ne prominent

e%ample of such a service is the ta%i-sharing application, offered by a global

telecom operator , where smart phone users can share a ta%i with other users at a

suitable location by revealing their departure and destination locations. )imilarly,

another popular service enables a group of users to find the most geographically

convenient place to meet.

Disadvantages0

1.2rivacy of a users 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. 3or instance, such information can be used to de-anonymize

users and their availabilities , to trac' their preferences or to identify their social

networ's. 3or e%ample, in the ta%i-sharing application, a curious third-party service


4/10

provider could easily deduce home4wor' location pairs of users who regularly use

their service.

.$ithout 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 trac' users location information in order to improve

the offered service can inadvertently harm users privacy, if the collected data is

lea'ed in an unauthorized fashion or improperly shared with corporate partners.

Proposed System:

$e then propose two algorithms for solving the above formulation of the 3562

problem in a privacy-preserving fashion, where each user participates by providing

only a single location preference to the 3562 solver or the service provider.

#n this significantly e%tended version of our earlier conference paper ,we evaluate

the security of our proposal under various passive and active adversarial scenarios,

including collusion. $e 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. #n 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 &o'ia mobile devices. $e

also address the multi-preference case, where each user may have multiple

prioritized location preferences. $e highlight the main differences, in terms of

performance, with the single preference case, and also present initial e%perimental

results for the multi-preference implementation. 3inally, by means of a targeted

user study, we provide insight into the usability of our proposed solutions.


5/10

Advantages0

$e address the privacy issue in *))s by focusing on a specific problem called

the Fair Rendez-Vous Point (FRVP) problem. 7iven a set of user location

preferences, the 3562 problem is to determine a location among the proposed ones

such that the ma%imum distance between this location and all other users locations

is minimized, i.e. it isfair to all users.

Goal0

/ur goal is to provide practical privacy preserving techniques to solve the 3562

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 3562 problem is representative of the relevant privacy threats in

*))s.

Algorithms:

/ur proposed algorithms ta'e advantage of the homomorphic properties of well-

'nown cryptosystems, such as 7&, El7amal and 2aillier, in order to privately

compute an optimally fair rendez-vous point from a set of user location

preferences.

Implementation Modules:


6/10

1 !ser Privacy

" Server Privacy

# PP$%&P protocol

' Privacy !nder Multiple DependentExecutions

!ser Privacy:

The user-privacy of any 223562 algorithm + measures the probabilistic advantage

that an adversary a gains towards learning the preferred location of at least one

other user ,e%cept the final fair rendez-vous location, after all users have

participated in the e%ecution of the 223562 protocol. +n adversary in this case is a

user participating in +. $e e%press user-privacy as three different probabilistic

advantages.

1. we measure the probabilistic advantage of an adversary ua in correctly

guessing the preferred locationLi of any user ui89 ua. This is referred to as

the identifiabilityadvantage.

. 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, between any two participating users ui89 u , is

greater than a given parameter s, without learning any users preferred

locationsLi , L .


7/10

:. The coordinate-linkability advantage, denoted as !dvc;L"#a , is the

probabilistic advantage of an adversary ua in correctly guessing whether a

given coordinate $i (or yi of a user ui is greater than the corresponding

coordinate(sof another user u 89 ui without learning the users preferred

locationsLi , L .

Server Privacy:

3or the third-party (*


8/10

PP$%&P protocol:

The 223562 protocol (shown in 3ig. > has three main modules0

(+ the distance computation module,

( the ?+@ module and

%) &istance 'outation* The distance computation module uses either the 7&-

distance or the 2aillier- El7amal distance protocols. $e note that modules ( and

(A use the same encryption scheme as the one used in module (+. #n other words,

(+).t refers to encryption using either the 7& or the 2aillier encryption scheme.) !/ 'outation* #n )tep .1, the *


9/10

which the information across e%ecutions is completely uncorrelated (e.g., different

set of users in each e%ecution or different and unrelated preferences in each

e%ecution reduce to independent e%ecution. $e analyze two different scenarios of

dependent

e%ecutions involving differential information .3irst, we consider the case of

dependent e%ecutions with different subsets of participants. $e assume that, in

each sequential e%ecution, the set of users or participants is reduced by e%actly one

(the adversary participant remains until the end, and that the retained participants

preferences remain the same as the previous e%ecution(s. The following

information is implicitly passed across e%ecutions in this scenario0

(i participant set,

(ii optimal fair locationL f air ,

(iii permuted and randomly scaled pair wise distances from

the participant to every other participant, and (iv scaled (but order preserving

ma%imum distance from every participant to every other participant.

System Confguration:-

H/W System Confguration:-

Processor - Pentium !!!

Spee" - #$# %&'

()M - *+, M.min


10/10

Har" Dis0 - *1 %

2loppy Drive - #$33 M

4ey oar" - Stan"ar" Win"o5s 4eyboar"

Mouse - 65o or 6&ree utton Mouse

Monitor - S7%)

S/W System Confguration:-

/perating )ystem 0$indowsBC4BD44@2

3ront End 0 !ava, !d'1.

Documents

IEEE 2014 JAVA MOBILE COMPUTING PROJECT Privacy-Preserving Optimal Meeting Location Determination on Mobile Devices