Abstract: In recent years, researchers have conducted in-depth studies on location anonymity-based privacy protection methods amidst the issue of privacy leakage in location-based services (LBS). However, these studies overlook the performance and security bottlenecks inherent in the anonymity process during collaboration, as well as the potential for privacy leakage in anonymous sets due to attacks lever- aging semantic knowledge. To address these issues, this paper proposes a distributed anonymous location privacy protection method based on multi-blockchain collaboration, integrating the concepts of cross-chain collaboration across multiple blockchains and k-anonymity. In this approach, firstly, to tackle the privacy leakage caused by centralized anonymity, this paper present a method for selecting anonymous collaboration users based on cross-chain collaboration between private and public blockchains. Secondly, to ensure the reliability of user collaboration behavior during anonymity and the correctness of cross-chain data transmission, designing an anonymous collaboration consensus mechanism. Lastly, to mitigate privacy leakage arising from overlooked individual-related semantics,  this paper devises an anonymous set construction method that combines differential privacy mechanisms with semantic diversity entropy for selecting anonymous locations. Experiments conducted on real-world datasets demonstrate that the proposed  method can effectively enhance the semantic privacy security of locations, outperforming existing methods in terms of privacy and usability.

Key words: blockchain, semantic attack, privacy protection