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 Cryp...

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Veröffentlicht in:	2020 12th International Conference on Knowledge and Systems Engineering (KSE) S. 91 - 96
Hauptverfasser:	Van Vu, Thi, Luong, The Dung, Hoang, Van Quan
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 12.11.2020
Schlagworte:	2-part fully distributed setting Elliptic curve cryptosystem Privacy preserving frequency computation Privacy-preserving data mining Secure multi-party computation
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Zusammenfassung:	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.
DOI:	10.1109/KSE50997.2020.9287423