IEEE 2014 JAVA MOBILE COMPUTING PROJECTS Preserving location privacy in geo social applications
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - email@example.com-Visit Our Website: www.finalyearprojects.org
Published on: Mar 4, 2016
Transcripts - IEEE 2014 JAVA MOBILE COMPUTING PROJECTS Preserving location privacy in geo social applications
IEEE 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 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
Visit: www.finalyearprojects.org Mail to:firstname.lastname@example.org
Preserving Location Privacy in Geo-Social
Using geo-social applications, such as FourSquare, millions of people interact with
their surroundings through their friends and their recommendations. Without
adequate privacy protection, however, these systems can be easily misused, e.g., to
track users target them for home invasion. In this paper, we introduce LocX, a
novel alternative that provides significantly-improved locationprivacy without
adding uncertainty into query results or relying on strong assumptions about server
security. Our key insight is to applysecure user-specific, distance-preserving
coordinate transformations to all location data shared with the server. The friends
of a usershare this user’s secrets so they can apply the same transformation. This
allows all location queries to be evaluated correctly by theserver, but our privacy
mechanisms guarantee that servers are unable to see or infer the actual location
data from the transformeddata or from the data access. We show thatLocX
provides privacy even against a powerful adversary model, and we use
prototypemeasurements to show that it provides privacy with very little
performance overhead, making it suitable for today’s mobile devices.
Existing systems have mainly taken three approaches to improving user privacy in
(a) introducinguncertainty or error into location data .
(b) relying on trusted servers or intermediaries to apply anonymization to user
identities and private data.
(c) relying on heavy-weight cryptographic or private information retrieval (PIR)
The challenge, then, is to design mechanisms that efficiently protect user privacy
without sacrificing the accuracy of the system, or making strong assumptions about
the security or trust worthiness of the application servers. More specifically, we
target geo-social applications, and assume that servers (and any intermediaries) can
be compromised and, therefore, are untrusted.
To address this challenge, in this paper, we propose LocX (short for location to
index mapping), a novel approach to achieving user privacy while maintaining full
accuracy in location-based social applications (LBSAs from here onwards).
Our insight is that many services do not need to resolve distance-based queries
between arbitrary pairs of users, but only between friends interested in each other’s
locations and data. Thus, we can partition location data based on users’ social
groups, and then perform transformations on the location coordinates before
storing them on un trusted servers. A user knows the transformation keys of all her
friends, allowing her to transform her query into the virtual coordinate system that
her friends use. Our coordinate transformations preserve distance metrics, allowing
an application server to perform both point and nearest-neighbor queries correctly
on transformed data. However, the transformation is secure, in that transformed
values cannot be easily associated with real world locations without a secret, which
is only available to the members of the social group. Finally, transformations are
efficient, in that they incur minimal overhead on the LBSAs. This makes the
applications built on LocX lightweight and suitable for running on today’s mobile
Loc X builds on top of the basic design, and introduces two new mechanisms to
overcome its limitations. First, in Loc X, we split the mapping between the
location and its data into two pairs: a mapping from the transformed location to an
encrypted index (called L2I), and a mapping from the index to the encrypted
location data (called I2D). This splitting helps in making our system efficient.
Second, users store and retrieve the L2Is via untrusted proxies. This redirection of
data via proxies, together with splitting, significantly improves privacy in LocX.
For efficiency, I2Ds are not proxied, yet privacy is preserved (as explained later).
Proxying L2Is for location privacy:
Users store their L2Ison the index server via untrusted proxies. These proxies can
be any of the following: Planet Lab nodes, corporate NAT sand email servers in a
user’s work places, a user’s home and office desktops or laptops, or Tor 
nodes. We only need a one-hop indirection between the user and the index server.
These diverse types of proxies provide tremendous flexibility in proxying L2Is,
thus a user can store her L2Is via different proxies without restricting herself to a
single proxy. Furthermore, compromising these proxies by an attacker does not
break users’ location privacy, as (a) the proxies also only see transformed location
coordinates and hence do not learn the users’ real locations, and (b) due to the
noise added toL2Is (described later). To simplify the description, for now, we
assume that the proxies are non-malicious and do not collude with the index server.
But we will later describe our solution in detail to even defend against colluding,
malicious proxies. With this high-level overview, we now describe our solution to
store and query data on the servers in detail. We also explain the challenges we
faced, and the tradeoffs we made in making
our solution secure and efficient.
Storing L2I on the index server:
First consider storing L2I on the index server. This transformation preserves the
distances between points1, so circular range and nearest neighbor queries for a
friend’s location data can be processed in the same way on transformed
coordinates as on real-world coordinates. Then the user generates a random index
(i) using her random number generator and encrypts it with her symmetric key to
obtain at the transformed coordinate on the index server via a proxy. The L2I is
small in size and is application independent, as it always contains the coordinates
and an encrypted random index. Thus the over head due to proxying is very small.
Storing I2Ds on the data server:
The user can directly storeI2Ds (location data) on the data server. This is both
secure and efficient.
1) This is secure because the data server only sees the index stored by the user and
the corresponding encrypted blob of data. In the worst case, the data server can
link all the different indices to the same user device, and then link these indices to
the retrieving user’s device. But this only reveals that one user is interested in
another user’s data, but not any information about the location of the users, or the
content of the I2Ds, or the real-world sites to which the data in the encrypted blob
2) The content of I2Dis application dependent. For example, a location-based
video or photo sharing service might share multiple MBs of data at each location.
Since this data is not proxied, LocX still maintains the efficiency of today’s
In this we use Locx Mechanisms is used in this project.
1) Alice and Bob exchange their secrets,
2) Alice generates and L2I and I2D from her review of the restaurant (at (x, y)),
and stores the L2I on the index server via a proxy.
3) She then stores the I2D on the data server directly.
4) Bob later visits the restaurant and fetches for L2Is from his friends by sending
the transformed coordinates via a proxy.
5) he decrypts the L2I obtained and then queries for the corresponding I2D, 6)
finally Bob decrypts Alice’s review.
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.