A Critical Look into Threshold Homomorphic Encryption for Private Average Aggregation

• URL: http://arxiv.org/abs/2602.22037v1
• Date: Wed, 25 Feb 2026 15:45:27 GMT
• Title: A Critical Look into Threshold Homomorphic Encryption for Private Average Aggregation
• Authors: Miguel Morona-Mínguez, Alberto Pedrouzo-Ulloa, Fernando Pérez-González,
• Abstract summary: Threshold Homomorphic Encryption (Threshold HE) is a good fit for implementing private federated average aggregation.<n>Recent studies have shown that threshold schemes can introduce unexpected security vulnerabilities if an adversary has access to a restricted decryption oracle.
• Score: 45.381652768314375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Threshold Homomorphic Encryption (Threshold HE) is a good fit for implementing private federated average aggregation, a key operation in Federated Learning (FL). Despite its potential, recent studies have shown that threshold schemes available in mainstream HE libraries can introduce unexpected security vulnerabilities if an adversary has access to a restricted decryption oracle. This oracle reflects the FL clients' capacity to collaboratively decrypt the aggregated result without knowing the secret key. This work surveys the use of threshold RLWE-based HE for federated average aggregation and examines the performance impact of using smudging noise with a large variance as a countermeasure. We provide a detailed comparison of threshold variants of BFV and CKKS, finding that CKKS-based aggregations perform comparably to BFV-based solutions.

Related papers

• Post-Quantum Secure Aggregation via Code-Based Homomorphic Encryption [19.264286608481296]
  We present a code-based alternative for secure aggregation based on key- and message-additive homomorphic encryption.<n>Our construction employs a committee-based decryptor realized via secret sharing.<n>We evaluate performance and identify regimes in which our approach outperforms information-theoretically secure aggregation protocols.
  arXiv  Detail & Related papers  (2026-01-19T13:14:01Z)
• Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems [89.35169042718739]
  collaborative inference enables end users to leverage powerful deep learning models without exposure of sensitive raw data to cloud servers.<n>Recent studies have revealed that these intermediate features may not sufficiently preserve privacy, as information can be leaked and raw data can be reconstructed via model inversion attacks (MIAs)<n>This work first theoretically proves that the conditional entropy of inputs given intermediate features provides a guaranteed lower bound on the reconstruction mean square error (MSE) under any MIA.<n>Then, we derive a differentiable and solvable measure for bounding this conditional entropy based on the Gaussian mixture estimation and propose a conditional entropy algorithm to enhance the inversion robustness
  arXiv  Detail & Related papers  (2025-03-01T07:15:21Z)
• FRAG: Toward Federated Vector Database Management for Collaborative and Secure Retrieval-Augmented Generation [1.3824176915623292]
  This paper introduces textitFederated Retrieval-Augmented Generation (FRAG), a novel database management paradigm tailored for the growing needs of retrieval-augmented generation (RAG) systems. FRAG enables mutually-distrusted parties to collaboratively perform Approximate $k$-Nearest Neighbor (ANN) searches on encrypted query vectors and encrypted data stored in distributed vector databases.
  arXiv  Detail & Related papers  (2024-10-17T06:57:29Z)
• EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters [3.9660142560142067]
  Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. FL remains vulnerable to inference attacks during model update transmissions. We present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding.
  arXiv  Detail & Related papers  (2024-06-13T14:16:50Z)
• Certifiably Byzantine-Robust Federated Conformal Prediction [49.23374238798428]
  We introduce a novel framework Rob-FCP, which executes robust federated conformal prediction effectively countering malicious clients. We empirically demonstrate the robustness of Rob-FCP against diverse proportions of malicious clients under a variety of Byzantine attacks.
  arXiv  Detail & Related papers  (2024-06-04T04:43:30Z)
• Differentially Private Clustered Federated Learning [4.768272342753616]
  Federated learning (FL) often incorporates differential privacy (DP) to provide rigorous data privacy guarantees.<n>Previous works attempted to address high structured data heterogeneity in vanilla FL settings through clustering clients (a.k.a clustered FL)<n>We propose an algorithm for differentially private clustered FL, which is robust to the DP noise in the system and identifies the underlying clients' clusters correctly.
  arXiv  Detail & Related papers  (2024-05-29T17:03:31Z)
• RecDCL: Dual Contrastive Learning for Recommendation [65.6236784430981]
  We propose a dual contrastive learning recommendation framework -- RecDCL. In RecDCL, the FCL objective is designed to eliminate redundant solutions on user-item positive pairs. The BCL objective is utilized to generate contrastive embeddings on output vectors for enhancing the robustness of the representations.
  arXiv  Detail & Related papers  (2024-01-28T11:51:09Z)
• AHSecAgg and TSKG: Lightweight Secure Aggregation for Federated Learning Without Compromise [7.908496863030483]
  AHSecAgg is a lightweight secure aggregation protocol using additive homomorphic masks. TSKG is a lightweight Threshold Signature based masking key generation method.
  arXiv  Detail & Related papers  (2023-12-08T10:06:17Z)
• Differentially Private Federated Clustering over Non-IID Data [59.611244450530315]
  clustering clusters (FedC) problem aims to accurately partition unlabeled data samples distributed over massive clients into finite clients under the orchestration of a server. We propose a novel FedC algorithm using differential privacy convergence technique, referred to as DP-Fed, in which partial participation and multiple clients are also considered. Various attributes of the proposed DP-Fed are obtained through theoretical analyses of privacy protection, especially for the case of non-identically and independently distributed (non-i.i.d.) data.
  arXiv  Detail & Related papers  (2023-01-03T05:38:43Z)
• Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond [57.10914865054868]
  We consider vertical logistic regression (VLR) trained with mini-batch descent gradient. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
  arXiv  Detail & Related papers  (2022-07-19T05:47:30Z)
• Learning, compression, and leakage: Minimising classification error via meta-universal compression principles [87.054014983402]
  A promising group of compression techniques for learning scenarios is normalised maximum likelihood (NML) coding. Here we consider a NML-based decision strategy for supervised classification problems, and show that it attains PAC learning when applied to a wide variety of models. We show that the misclassification rate of our method is upper bounded by the maximal leakage, a recently proposed metric to quantify the potential of data leakage in privacy-sensitive scenarios.
  arXiv  Detail & Related papers  (2020-10-14T20:03:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.