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).How-ever,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 miti-gate privacy leakage arising from overlooked individual-related semantics,this paper devises an anony-mous set construction method that combines differential privacy mechanisms with semantic diversity en-tropy for selecting anonymous locations.Experiments conducted on real-world datasets demonstrate that the proposed method can effectively enhance the semantic privacy security of locations,outperfor-ming existing methods in terms of privacy and usability.
维普
万方数据