A privacy preserving homomorphic computing toolkit for predictive computation
作者：
Highlights：
• We use the Paillier encryption system and Lagrange's interpolation theorem to construct a new threshold public key cryptosystem with trapdoor, this can lower the risk of private key leakage and the cost of private key management.
• We build an outsourced computing toolkit to protect data privacy. The toolkit includes commonly used basic operations, such as addition, subtraction, multiplication, division, comparison, sign bit extraction, equivalence testing etc.
• We extend the toolkit to design a secure encryption scheme for polynomial function and its derivation, we also discuss its application in the secure active function of neural network.
• We use rigorous mathematical analysis to verify the security and correctness of our schemes, we also evaluate the efficiency of these schemes through theoretical analysis and implementation.
摘要
•We use the Paillier encryption system and Lagrange's interpolation theorem to construct a new threshold public key cryptosystem with trapdoor, this can lower the risk of private key leakage and the cost of private key management.•We build an outsourced computing toolkit to protect data privacy. The toolkit includes commonly used basic operations, such as addition, subtraction, multiplication, division, comparison, sign bit extraction, equivalence testing etc.•We extend the toolkit to design a secure encryption scheme for polynomial function and its derivation, we also discuss its application in the secure active function of neural network.•We use rigorous mathematical analysis to verify the security and correctness of our schemes, we also evaluate the efficiency of these schemes through theoretical analysis and implementation.
论文关键词：Predictive computation,Homomorphic encryption,Data outsourcing and processing,Data management
论文评审过程：Received 29 October 2021, Revised 15 January 2022, Accepted 17 January 2022, Available online 7 February 2022, Version of Record 7 February 2022.
论文官网地址：https://doi.org/10.1016/j.ipm.2022.102880
