Publicación: A BATCHING LOCATION CLOAKING ALGORITHM FOR LOCATION PRIVACY PROTECTION
Fecha
2018
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COLLABORATIVE TECHNOLOGIES AND DATA SCIENCE IN SMART CITY APPLICATIONS
Resumen
MANY REPORTERS HAVE HIGHLIGHTED THAT LOCATION-BASED SERVICES (LBS) HAVE OPENED SEVERAL QUESTIONS CONCERNING PRIVACY. WHEN A CLIENT RELEASES HER LOCATION TO A LBS, SHE COULD PUT HERSELF IN DANGER. TO MITIGATE THIS ISSUE, RESEARCHERS HAVE PROPOSED SEVERAL LOCATION CLOAKING TECHNIQUES. HOWEVER, THESE SOLUTIONS HAVE SEVERAL DRAWBACKS. MOST OF THEM ARE BASED ON AN ANONYMIZER AND THEY DO NOT ADDRESS THE SCALABILITY ISSUES FACED BY THE SERVER WHEN A HIGH DEMAND FOR LOCATION PRIVACY PROTECTION IS REQUESTED. ALSO, THEY DO NOT CONSIDER THE POTENTIAL NEGATIVE IMPACT ON THE LBS WHEN POSTERIORI PROCESSING OF MANY LOCATIONCLOAKED QUERIES (LCQ). THIS PAPER CONSIDERS THE PROBLEMS OF EFFICIENT CONSTRUCTION OF LOCATION CLOAKING AREAS WHEN A TRUSTED ANONYMIZER NEEDS TO PROVIDE LOCATION PRIVACY PROTECTION FOR HETEROGENEOUS LBS CLIENTS AND TO LIMIT THE COST OF PROCESSING MANY LCQS AT THE LBS. OUR KEY GOAL IS TO BUILD SHARED CLOAKING REGIONS FOR LBS USERS LOCATED NEARBY EACH OTHER AND HAVING SIMILAR PRIVACY CONCERNS. WE PROPOSE SEVERAL BATCHING APPROACHES FOR BUILDING PROPER CLOAKING REGIONS. THROUGHOUT EXTENSIVE SIMULATIONS, WE WILL SHOW OUR APPROACH CAN BALANCE BOTH THE ANONYMIZER WORKLOAD AND LBS WORKLOAD.
