Attacking algorithm against k-anonymized continuous queries in location based service

K-anonymization cannot effectively protect anonymity of continuous queries in location-based service (LBS). A continuous query issuing model aimed at the problem was proposed. The model incorporated a query issuing interval model and a consecutive queries relationship model. An attacking algorithm aimed at the k-anonymization algorithm was presented based on the model. The algorithm associated a series of snapshots related to continuous queries in order to calculate the probability of each user in the anonymity-set. Then the true query sender was identified by choosing the user with the highest probability. K-anonymized queries were re-identified with different continuity arguments and cardinalities of anonymity-set. Experiments demonstrate that the algorithm has high success rate (85%) in identifying query senders when the continuous queries have strong relationship, which is 1.5 times higher than the success rate without the attacking algorithm and severely undermines the anonymity of the queries.