An Elliptic Curve-based Protocol for Privacy Preserving Frequency Computation in 2-Part Fully Distributed Setting

Privacy-preserving frequency computation is critical to privacy-preserving data mining in 2-Part Fully Distributed Setting (such as association rule analysis, clustering, and classification analysis) and has been investigated in many researches. However, these solutions are based on the Elgamal Cryptosystem, making computation and communication efficiency low. Therefore, this paper proposes an improved protocol using an Elliptic Curve Cryptosystem. The theoretical and experimental analysis shows that the proposed method is effective in both computing and communication compared to other methods.