Privacy Protection Model for Location-Based Services
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초록
Solving the disclosure problem of sensitive information with the k-nearest neighbor query, location dummytechnique, or interfering data in location-based services (LBSs) is a new research topic. Although they reducedsecurity threats, previous studies will be ineffective in the case of sparse users or K-successive privacy, andadditional calculations will deteriorate the performance of LBS application systems. Therefore, a model isproposed herein, which is based on geohash-encoding technology instead of latitude and longitude, memcachedserver cluster, encryption and decryption, and authentication. Simulation results based on PHP and MySQLshow that the model offers approximately 10× speedup over the conventional approach. Two problems aresolved using the model: sensitive information in LBS application is not disclosed, and the relationship betweenan individual and a track is not leaked.
Abstract
Solving the disclosure problem of sensitive information with the k-nearest neighbor query, location dummytechnique, or interfering data in location-based services (LBSs) is a new research topic. Although they reducedsecurity threats, previous studies will be ineffective in the case of sparse users or K-successive privacy, andadditional calculations will deteriorate the performance of LBS application systems. Therefore, a model isproposed herein, which is based on geohash-encoding technology instead of latitude and longitude, memcachedserver cluster, encryption and decryption, and authentication. Simulation results based on PHP and MySQLshow that the model offers approximately 10× speedup over the conventional approach. Two problems aresolved using the model: sensitive information in LBS application is not disclosed, and the relationship betweenan individual and a track is not leaked.
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