Homomorphic encryption location privacy-preserving scheme based on Markov model

Abstract

Homomorphic encryption location privacy-preserving scheme based on Markov mode was proposed to solve the problem of location privacy and query privacy protection in location-based service systems. Firstly, the anonymous user's identity were permuted randomly and the Markov state transition matrix combining with the user's historical query content was constructed. Secondly, system previously queries the user's high frequency con-tent and the prediction content under Markov chain, then store the corresponding result sets. Finally, the security of the scheme's double prediction system was analyzed. The scheme makes the LBS receives k+1 query contents which let malicious server or attacker can't determine the corresponding relation between queried user's real identity and queried content. So the user's location privacy and query privacy can be protected. Meanwhile, the computability and confidentiality of homomorphic encryption ciphertext were used to realize the statistical analysis of cipher-text-oriented data and the secure storage of private data.

Keywords

• location-based services
• markov chain
• homomorphic encryption
• anonymity
• inquiry privacy