TMI-VFL: Secure Vertical Federated Learning via Threshold Multi-Identity Homomorphic Encryption

PNFR: practicality-enhanced and non-interactive privacy-preserving federated regressions

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 incurring massive computational and communication overhead. In 2020, Wang et al. first proposed a non-interactive federated regression scheme, which effectively improves the training efficiency of regression models while protecting the privacy of local training data. However, like most current federated regressions, it involves a third authority (TA) to generate keys for each entity, which poses a significant privacy risk and results in considerable communication overhead. From the view of security and practicality, this paper first proposes a multi-party homomorphic encryption algorithm named MPaillier. Furthermore, we have designed PNFR, a privacy-preserving federated learning scheme for regressions training built on the MPaillier algorithm. The participating entities of PNFR are the data owners and a cloud server, eliminating the need for a TA, thus enhancing the practicality and efficiency of the scheme. Experimental results demonstrate that our scheme is $\sim 10^{3}$ times faster than interactive federated regressions PrivFL and about 80% faster than non-interactive federated regressions VANE.

SecureDrive-FL: Joint Differential Privacy and Gradient-Aware Selective Homomorphic Encryption for Federated Driver Monitoring

This work introduces GASHE (Gradient-Aware Selective Homomorphic Encryption), a novel selective encryption strategy that dynamically identifies and encrypts only the gradient components exceeding a DP-calibrated sensitivity threshold, rather than encrypting all parameters uniformly as in static layer-based or full-parameter CKKS schemes.

Transferability of Evasion and Backdoor Attacks in Encrypted Domain

NETFL: Network Efficient Fully Secure Federated Learning at Scale

Federated Learning (FL) enables collaborative model training without centralizing client data, making it well-suited for privacy-sensitive domains. Existing approaches use techniques such as homomorphic encryption, differential privacy, and secure multi-party computation to mitigate attacks including model inversion, membership inference, and gradient leakage. However, these methods often incur high computational and memory overheads and frequently overlook confidentiality of the global model itself, which may be proprietary and sensitive. These limitations reduce the practicality of secure FL in large-scale and compliance-sensitive environments.We present NETFL, a Fully Secure and scalable FL framework that decentralizes training across client pairs using lightweight MPC, while restricting servers to secure aggregation, client pairing, and routing. NETFL eliminates server-side bottlenecks, avoids full data offloading, and preserves confidentiality of data, model parameters, and updates throughout training. Our evaluation shows that NETFL protects against unauthorized observation, reconstruction, gradient leakage, membership inference, and inversion attacks, while achieving up to 13× faster training time and 50% lower server memory usage compared to prior work.

Quantum-Secure Data Pseudonymization Framework with QKD-based Key Management and ML-Driven Re-Identification Risk Analysis

The increasing needs in data sharing in the fields of finance, governance, and artificial intelligence pose a major threat to privacy, particularly in quantum computing. In this paper, a hybrid privacy-preserving system incorporating simulated BB84 Quantum Key Distribution (QKD) to generate secure keys, reversible pseudonymization with encrypted mapping vaults, automatic key rotation, and re-identification risk analysis by machine learning are introduced. Also, optional differential privacy layer allows irreversible anonymization in the cases of analysis. The proposed system will enable two modes, that is, recovery of secure data and the ability to publish data in privacy modes. The experimental findings indicate that authorized users have 100% recovery accuracy, re-identification risk is low and is close to random guessing and data utility is acceptable given the privacy restrictions. FastAPI and Streamlit are used to implement the framework, which is appropriate in the real-world deployment in clouds.

A Maliciously Secure and Fully Decentralized Threshold FHE Scheme with Native RNS Acceleration

: Threshold fully homomorphic (ThFHE) encryption, as a communication encryption protocol, ensures that no third party participates in generating or knows any parameters. Compared to multi-key fully homomorphic encryption, it avoids excessive noise expansion caused by too many users participating in the calculation. However, current ThFHE algorithms focus on reducing computational overhead, thereby neglecting integrity verification of participating nodes’ behavior in distributed collaborative environments, leaving the system vulnerable to malicious actors. Without an effective verification mechanism, malicious nodes can manipulate the final result without breaking the protocol flow by injecting biased noise or providing forged partial decryption values, compromising data integrity. This research proposes an enhanced ThFHE encryption scheme based on a Full Remainder System (Full-RNS) architecture. This scheme integrates Distributed Key Generation (DKG) and Multi-Party Computation Relinearized Key (MPC RLK) techniques to achieve fully decentralized parameter initialization. To combat malicious attacks, we introduce a Non-Interactive Zero-Knowledge (NIZK) proof that incorporates smudging noise, ensuring that the computational trajectory at each stage can be publicly verified without leaking private key information. The results of the experiment show that this scheme maintains efficient homomorphic computation of the BFV algorithm while effectively resisting node fraud, providing more robust security for voting systems and medical privacy computations.