PROFILR : Toward Preserving Privacy and Functionality in
Geosocial Networks
ABSTRACT:
Profit is the
main participation incentive for social network providers. Its reliance on user
profiles, built from a wealth of voluntarily revealed personal information,
exposes users to a variety of privacy vulnerabilities. In this paper, we
propose to take first steps toward addressing the conflict between profit and
privacy in geosocial networks. We introduce PROFILR, a framework for
constructing location centric profiles (LCPs), aggregates built over the
profiles of users that have visited discrete locations (i.e., venues). P ROFIL
R endows users with strong privacy guarantees and providers with correctness
assurances. In addition to a venue centric approach, we propose a decentralized
solution for computing real time LCP snapshots over the profiles of colocated
users. An Android implementation shows that P ROFIL R is efficient; the
end-to-end overhead is small even under strong privacy and correctness assurances.
EXISTING SYSTEM:
Online social
networks have become a significant source of personal information. Their users
voluntarily reveal a wealth of personal data, including age, gender, contact
information, preferences and status updates. A recent addition to this space,
geosocial networks (GSNs) such as Yelp and Foursquare further collect fine
grained location information, through check-ins performed by users at visited
venues. Overtly, personal information allows GSN providers to offer a variety
of applications, including personalized recommendations and targeted
advertising, and venue owners to promote their businesses through
spatio-temporal incentives, e.g., rewarding frequent customers through
accumulated badges
DISADVANTAGES
OF EXISTING SYSTEM:
·
Providing
personal information exposes however users to significant risks.
·
As
social networks have been shown to leak and even sell user data to third
parties.
PROPOSED SYSTEM:
First, we
propose a venue centric P ROFIL R , that relieves the GSN provider from a
costly involvement in venue specific activities. To achieve this, P ROFIL R
stores and builds LCPs at venues. Furthermore, it relies on Benaloh’s
homomorphic cryptosystem and zero knowledge proofs to enable oblivious and
provable correct LCP computations. We prove that P ROFIL R satisfies the
introduced correctness and privacy properties.
Second, we
propose a completely decentralized PROFIL R extension, built around the notion
of snapshot LCPs. The distributed P ROFIL R enables user devices to aggregate
the profiles of co-located users, without assistance from a venue device.
Snapshot LCPs are not bound to venues, but instead user devices can compute
LCPs of neighbors at any location of interest. Communications in both PROFILR
implementations are performed over ad hoc wireless connections.
ADVANTAGES
OF PROPOSED SYSTEM:
·
Privacy
preserving, personalized public safety recommendations and
·
Privately
building real time statistics over the profiles of venue patrons with Yelp
accounts.
·
Evaluate
PROFIL R through an Android implementation. Show that PROFILR is efficient even
when deployed on previous generation Smartphones.
SYSTEM
REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø
System : Pentium IV 2.4 GHz.
Ø
Hard Disk :
40 GB.
Ø
Floppy Drive : 1.44
Mb.
Ø
Monitor : 15
VGA Colour.
Ø
Mouse :
Logitech.
Ø Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
Ø Operating system : Windows
XP/7.
Ø Coding Language : JAVA/J2EE
Ø IDE : Netbeans 7.4
Ø Database : MYSQL
REFERENCE:
Bogdan Carbunar,
Mahmudur Rahman, Jaime Ballesteros, Naphtali Rishe, and Athanasios V. Vasilakos
“PROFILR : Toward Preserving Privacy and Functionality in Geosocial
Networks” IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 9, NO.
4, APRIL 2014.
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