Efficient Privacy-Preserving Computation Based on Additive Secret Sharing
This work addresses privacy issues in cloud computing for users outsourcing sensitive data, representing an incremental improvement over existing secure multiparty computation methods.
The paper tackles the problem of privacy-preserving computation in cloud computing by proposing a secure 2-party computation scheme based on secret sharing, achieving high efficiency and universal composability in the honest-but-curious model.
The emergence of cloud computing provides a new computing paradigm for users -- massive and complex computing tasks can be outsourced to cloud servers. However, the privacy issues also follow. Fully homomorphic encryption shows great potential in privacy-preserving computation, yet it is not ready for practice. At present, secure multiparty computation (MPC) remains mainly approach to deal with sensitive data. In this paper, following the secret sharing based MPC paradigm, we propose a secure 2-party computation scheme, in which cloud servers can securely evaluate functions with high efficiency. We first propose the multiplicative secret sharing (MSS) based on typical additive secret sharing (ASS). Then, we design protocols to switch shared secret between MSS and ASS, based on which a series of protocols for comparison and nearly all of the elementary functions are proposed. We prove that all the proposed protocols are Universally Composable secure in the honest-but-curious model. Finally, we will show the remarkable progress of our protocols on both communication efficiency and functionality completeness.