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Efficient Privacy-Preserving Computation Based on Additive Secret Sharing

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arxiv 2009.05356 v2 pith:PMXSRHVT submitted 2020-09-11 cs.CR

Lizhi Xiong , Wenhao Zhou , Zhihua Xia , Qi Gu , Jian Weng This is my paper · ORCID
classification cs.CR
keywords secretcomputationprotocolssharingcloudcomputingsecureadditive
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CryptGNN: Enabling Secure Inference for Graph Neural Networks

    cs.CR 2025-09 reject novelty 6.0 of 10

    CryptGNN lets a client run a third-party GNN model on sensitive graph data using secret-shared computations across multiple cloud servers, keeping both data and model private.

  2. SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding

    cs.CR 2025-05 conditional novelty 6.0 of 10

    SecEmb is a two-server protocol using function secret sharing that privately retrieves and aggregates only sparse item-embedding updates in federated recommender systems, cutting user communication and computation.

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