Hiding In The Mobile Crowd: Location Privacy Through Collaboration

HIDING IN THE MOBILE CROWD: LOCATION PRIVACY THROUGH COLLABORATION

ABSTRACT:

Location-aware smartphones support various location based services (LBSs): users query the LBS server and learn on the fly about their surroundings. However, such queries give away private information, enabling the LBS to track users. We address this problem by proposing a user-collaborative privacy preserving approach for LBSs. Our solution does not require changing the LBS server architecture and does not assume third party servers; yet, it significantly improves users’ location privacy. The gain stems from the collaboration of mobile devices: they keep their context information in a buffer and pass it to others seeking such information. Thus, a user remains hidden from the server, unless all the collaborative peers in the vicinity lack the sought information. We evaluate our scheme against the Bayesian localization attacks that allow for strong adversaries who can incorporate prior knowledge in their attacks. We develop a novel epidemic model to capture the, possibly time-dependent, dynamics of information propagation among users. Used in the Bayesian inference framework, this model helps analyze the effects of various parameters, such as users’ querying rates and the lifetime of context information, on users’ location privacy. The results show that our scheme hides a high fraction of location-based queries, thus significantly enhancing users’ location privacy. Our simulations with real mobility traces corroborate our model-based findings. Finally, our implementation on mobile platforms indicates that it is lightweight and the cost of collaboration is negligible.

EXISTING SYSTEM:

The need to enhance privacy for LBS users is understood and several solutions have been proposed, falling roughly into two main categories: centralized and user-centric. Centralized approaches introduce a third party in the system, which protects users’ privacy by operating between the user and the LBS. Such an intermediary proxy server could anonymize (and obfuscate) queries by removing any information that identifies the user or her device. Alternatively, it could blend a user’s query with those of other users, so that the LBS server always sees a group of queries. Other centralized approaches require the LBS to change its operation by, for example, mandating that it process modified queries (submitted in forms that are different from actual user queries, possibly encrypted using PIR), or that it store data differently (e.g., encrypted or encoded, to allow private access).

DISADVANTAGES OF EXISTING SYSTEM:

·       It has problem of protecting privacy of users who also want to earn the benefits of LBSs.

·       It has a chance to misuse the private data.

·       It is vulnerable to disclosure attacks.

PROBLEM STATEMENT:

All the information is collected by the LBS operators. So, they might be tempted to misuse their rich data by, e.g., selling it to advertisers or to private investigators.

SCOPE:

The novel epidemic model to capture, possibly time dependent, dynamics of information propagation among users.

PROPOSED SYSTEM:

The key idea of our scheme, called MobiCrowd, is that users only contact the LBS server if they cannot find the sought information among their peers, i.e., other nearby reachable user devices. Hence, users can minimize their location information leakage by hiding in the crowd. Clearly, MobiCrowd would be most effective when there are many peers gathered at the same location. Indeed, this clustering phenomenon has been observed in human mobility studies. Moreover, the places where people gather are points of interest, where users are most likely to query

LBS. Thus, MobiCrowd would be used exactly where it is most effective. We evaluate MobiCrowd through both an epidemic- based differential equation model and a Bayesian frame- work for location inference attacks.

ADVANTAGES OF PROPOSED SYSTEM:

·       It is more secure.

·       It will improve privacy, without the need for a trusted third-party (TTP).

·       It will not change in the LBS server architecture and its normal operation.

 

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

ü Processor                  -        Pentium –IV

ü Speed                        -        1.1 Ghz

ü RAM                         -        512 MB(min)

ü Hard Disk                 -        40 GB

ü Key Board                -        Standard Windows Keyboard

ü Mouse                       -        Two or Three Button Mouse

ü Monitor                     -        LCD/LED

SOFTWARE REQUIREMENTS:

•         Operating system :         Android

•         Coding Language :         Android

•         Data Base             :         SQLite

•         Tool                     :         Eclipse

REFERENCE:

Reza Shokri, George Theodorakopoulos, Panos Papadimitratos, Ehsan Kazemi,

Jean-Pierre Hubaux “Hiding in the Mobile Crowd: Location Privacy through Collaboration” IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, SPECIAL ISSUE ON “SECURITY AND PRIVACY IN MOBILE PLATFORMS”, 2014.