A blockchain-based transaction system with payment statistics and supervision
Liutao Zhao, Jiawan Zhang, Lin Zhong · Connection Science · 2022
Due to existing blockchain systems concentrate mainly on privacy protection but lack payment statistics and supervision, we propose a blockchain-based transaction system with payment statistics and supervision. In the system, a payer uses a homomorphic encryption scheme to protect payment amounts. After the transaction is recorded in the blockchain, not only the payee can decrypt the payment amounts and use for future payment, but also the payer can even decrypt it and use for payment statistics. Besides, two supervisors can independently decrypt all users' payment amounts to master the whole economic dynamism and detect illegal transactions. Comparing with existing schemes, our homomorphic scheme only increases a little length of ciphertext, but supports payment amounts decryption by the payer, an additional two receivers. Finally, analyses show that our system is extremely efficient.