The query privacy issue in location-based services (LBS) has recently received considerable attention. To protect the query privacy of users, current state-of-the-art techniques adopt cloaking which cloaks the sender's location to k-anonymized spatial region (ASR), such that an attack based on the sender's location cannot identify the query source with probability larger than 1/k, among other k-1 users. However, these techniques do not consider that these k-1 additional mobile users with different privacy requirements may send request at the same time. In this paper, we propose a new attack model that an adversary who observes all the ASRs at one time can identify the query source with probability larger than 1/k based on prior knowledge of the locations of all users. And we propose our two algorithms, namely, Basic and Adaptive Algorithms, which strengthen the privacy guarantee to defend such inference attack while still support personalized user privacy requirements. We verify the effectiveness of the proposed algorithms by experiments on location data which synthetically generated on real road maps.