A privacy preserving homomorphic computing toolkit for predictive computation

doi:10.1016/j.ipm.2022.102880

A privacy preserving homomorphic computing toolkit for predictive computation

2022-03-01

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DOI：10.1016/j.ipm.2022.102880

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Predictive computation now is a more and more popular paradigm for artificial intelligence. In this article, we discuss how to design a privacy preserving computing toolkit for secure predictive computation in smart cities. Predictive computation technology is very important in the management of cloud data in smart cities, which can realize intelligent computing and efficient management of cloud data in the city. Concretely, we propose a homomorphic outsourcing computing toolkit to protect the privacy of multiple users for predictive computation. It can meet the needs of large-scale users to securely outsource their data to cloud servers for storage, management and processing of their own data. This toolkit, using the Paillier encryption system and Lagrangian interpolation law, can implement most commonly basic calculations such as addition, subtraction, multiplication and division etc. It can also implement secure comparison of user data in the encrypted domain. In addition, we discuss how to implement the derivative of polynomial functions using our homomorphic computing encryption tool. We also introduce its application in neural networks. Finally, we demonstrate the security and efficiency of all our protocols through rigorous mathematical analysis and performance analysis. The results show that our toolkit is efficient and secure.

Keywords:

Predictive computation

Homomorphic encryption

Data outsourcing and processing

Data management

A privacy preserving homomorphic computing toolkit for predictive computation

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