Ke Zhang, Wei Jeng, Francis Fofie,Konstantinos Pelechrinis, Prashant Krishnamurthy

University of Pittsburgh

ACM LBSN 2012Pittsburgh, PA

Towards Reliable Spatial Information in LBSNs

Outline• Problem definition• Effects of fake check-ins• Fake check-in detection• Conclusion and future work

Location Sharing in LBSN

People can easily forge their whereabouts without proof of the locations…- Alter GPS’s API (FakeLocation)- Bypass localization module to manually check in a different venue than

the actual one

People usually use fake check-in to

Gain real rewards

Mislead others

Gain more virtual rewards

Our Goals and Contribution• Emphasize the effects of fake spatial information

in order to advocate the importance of identifying fake location sharing

• Provide a preliminary system based on location proof to detect fake check-ins

Fake Check-in Leads Monetary Losses…

• Local businesses utilize LBSN as an inexpensive marketing channel for advertisement

• Users can obtain special offers by checking-in to participating venues without their presence

Fake Check-in Results in Degraded Services..

• Noisy data will not guarantee high quality service• Foursquare provides recommendations by

considering check-ins fromo Userso Friends o Venues

• Fake location information degrades the quality of service

Related Efforts• Foursquare provides the “cheater code” to

minimize fake check-ins by imposing additional rules on users’ check-in frequency and speed

• In our work we will utilize the primitives of location proofs

Our Scheme• We consider nearby fake check-ins:

o Users check in to a locale that is nearby even if they are not physically present in it

• Three assumptions: o The number of fake check-ins are less than the true oneso True check-ins are spatially within the venue; fake

check-ins are largely distributed outside the venueo All devices have the same wireless capabilities

Location proofs

User needs to provide location proof along every check-ino Received Signal Strength (RSS) vector measured from nearby

WiFi APs

Check-in points

Location Verification

The LBSN provider utilizes recent k historical proofs provided by users who claims in the venue.

o Apply density clustering to RSS vector space

Check-in points Clusters Noise

Simulation Set Up

• Venues are grouped into blocks of 6 and arranged in a 2D plane separated by streetso 90% of the venues are randomly assigned a WiFi AP

• Users follow the RANK model to decide the next destination to check in

• A user with a fake check-in will be positioned randomly outside the venue

Wireless Channel Model

• Attenuation Factor Model for users to record RSS o : the signal strength at distanceo : path loss exponent o : wall attenuation factoro : number of obstacles along the patho : noise with Gaussian

Evaluation Results

• The performance is better when the wireless channel is stable• In a highly variable environment, our approach still performs

efficiently• Detection works better with smaller number of fake check-ins

Conclusions• We bring the attention to the community on the

effects of fake check-ins by analyzing various possible real-life situations

• We design and evaluate via simulations a prototype detection systemo Density clusteringo Location proofs

Future Directions• Implement our system on real hardware and

examineo Real world performanceo Effect of wireless hardware

• Investigate different – more generic- approaches that do not depend on the assumptions made

Thank you