Optimization of Privacy Preserving mechanisms in Mining Continuous Patterns

This work presents a new Apriori-like algorithm called CPMPP (Continuous Pattern Mining with Privacy Preservation) for exploring long continuous sequences in a distributed environment with privacy preservation. Given the fact that a cryptography-based technique may consume a considerable amount of time when enciphering data using a commutative cipher, a short form of locally frequent sequences has been applied to improve the system performance. In this approach, the length of locally frequent sequences does not exceed two items. In consequence, the proposed algorithm reduces the number of expensive cryptographic operations required to obtain the result. The conducted experiments show a significant performance increase with the increase of the length of generated patterns.