Abstract:

Privacy preserving is one of the most important topics in data mining. The purpose is to discover accurate rules and knowledge without precise access to the raw data. Its mining rules and knowledge are the same or similar with the plaintext data mining results. In order to enhance privacy preservation and improve data mining accuracy, the paper focuses on the privacy preserving problem of clustering data mining in a distributed environment, combines fully homomorphic encryption and decryption algorithms, and proposes a fully homomorphic encryption algorithm based on the FHEDBIRCH model. The model ensures data privacy when data transmission uses fully homomorphic encryption and decryption. Theoretical analysis and experimental results show that the FHEDBIRCH model can provide better privacy and accuracy.

Key words: data mining;privacy preserving;clustering;distributed database