Conclusions

Abstract

The serious privacy concerns that are raised due to the sharing of large transactional databases with untrusted third parties for association rule mining purposes, soon brought into existence the area of association rule hiding, a very popular subarea of privacy preserving data mining. Association rule hiding focuses on the privacy implications originating from the application of association rule mining to shared databases and aims to provide sophisticated techniques that effectively block access to sensitive association rules that would otherwise be revealed when mining the data. The research in this area has progressed mainly along three principal directions: (i) heuristic-based approaches, (ii) border-based approaches, and (iii) exact hiding approaches. Taking into consideration the rich work proposed so far, in this book we tried to collect the most significant research findings since 1999, when this area was brought into existence, and effectively cover each principal line of research. The detail of our presentation was intentionally finer on exact hiding approaches, since they comprise the most recent direction, offering increased quality guarantees in terms of distortion and side-effects introduced by the hiding process.