The proposed Secure Multiparty Computation protocol enables collaborative training of linear and logistic regression models while providing formal privacy guarantees for participant data, and adapts the iterative gradient descent algorithm to operate securely over secretly shared vectors.
Federated learning is a technology that is used to protect data privacy in machine learning. Nonetheless, in federated learning, updating the global model requires the use of gradient descent algorithm, which involves multiple rounds of interaction between entities to complete the iterative updates, inevitably incurr...
This paper revisits the continuous noise sampling protocols and makes several improvements in both security and efficiency and turns to discrete sampling at the granularity of individual biased bits to address the security and efficiency issues together.
Federated learning (FL) enables collaborative model training across multiple clients in a privacy-preserving manner. However, the employment of homomorphic encryption algorithms might lead to high computational cost while the application of differential privacy (DP) methods would sacrifice model performance. To establi...
Zhiqiang Chen, Yuchen Jiang, Ray Y. Zhong et al.· IEEE Transactions on Informa...· 0 citations
Federated learning faces three critical challenges in enabling cross-institutional collaboration: privacy leakage, poisoning by malicious clients, and unverifiable aggregation results. To address these issues in a unified manner, we propose PVeriFL—a federated learning framework that integrates privacy preservation, By...
As cyber threats grow increasingly sophisticated, collaborative AI-driven defense has become essential. Yet sharing threat intelligence across organizations conflicts with data privacy regulations (GDPR, CCPA) and competitive interests. This paper presents Privacy-Preserving AI Co-Defense (PPAICD): a comprehensive arch...
V. Babu, K. Krishnakumar· 2026 International Conferenc...· 0 citations
This paper presents a structured review of privacy-preserving data processing techniques for cloud environments built on HE and FL, individually and in hybrid combination, and identifies promising directions for future research.
Shivendra Shukla, C. S. Gautam, Divyansh Tiwari· International Journal of Cre...· 0 citations
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