Abstract:
Abstract： With the proliferation and development of Location-Based Services （LBS）， the privacy protection of user location data has become an urgent issue. Existing anonymity-based privacy protection methods face challenges related to the semantic information in anonymous locations and query content affecting anonymity security. To address this， we propose a Dual Privacy Protection Algorithm Based on Multi-Anonymizer Architecture （MAA-DPPA）， utilizing a verifiable secret sharing algorithm. Unlike existing algorithms， MAA-DPPA enhances location privacy by integrating location anonymity with encrypted queries. Initially， a verifiable secret sharing and multi-anonymizer query encryption algorithm is introduced to enhance query security. Furthermore， an anonymization method is designed to improve semantic location privacy by satisfying individual semantic diversity and replacing sensitive semantic locations. Experimental results demonstrate that this algorithm achieves the highest level of privacy. The average improvement percentage range of MAA-DPPA compared to algorithms based on road network semantic diversity anonymous set construction under multiple conditions is 38.3% ~ 59.8%， and compared to algorithms based on semantic and trade-off perception， the improvement range is 51.1% ~76.5%. MAA-DPPA significantly enhances privacy protection while improving algorithm efficiency， verifying its effectiveness in safeguarding user location privacy.

Key words: Key words： semantic location privacy； semantic attack； multi-anonymizer architecture； verifiable secret sharing ,