A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks.

ABSTRACT: Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of

SUBMITTER: Zhang H 

PROVIDER: S-EPMC5540611 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature