The Best Way to a Strong Defense is a Strong Offense: Mitigating Deanonymization Attacks via Iterative Language Translation
Nathan H. Mack, Jasmine D. Bowers, Henry Williams, Gerry Vernon Dozier, Joseph Shelton · International Journal of Machine Learning and Computing · 2015
In Bowers, et al., a technique was presented, referred to as Iterative Language Translation (ILT), for reducing the threat of deanonymization attacks via two well-known author identification systems (AISs).In this paper, we introduce four additional 'stronger' AISs, which outperform the AISs evaluated in Bowers, et al.Our results show that ILT still remains an effective technique for reducing author identification accuracy even if stronger AISs are used.