Pivot: Proactive and Verifiable Threshold Oblivious Pseudorandom Functions From Isogeny Group Actions
- URL: http://arxiv.org/abs/2608.01390v1
- Date: Sun, 02 Aug 2026 17:11:35 GMT
- Title: Pivot: Proactive and Verifiable Threshold Oblivious Pseudorandom Functions From Isogeny Group Actions
- Abstract summary: Oblivious pseudorandom functions (OPRFs) allow a client to evaluate a keyed pseudorandom function on a private input without revealing that input to the server.<n>Existing isogeny-based threshold OPRFs are primarily designed for static corruption models.<n>We introduce PIVOT, a dealerless threshold VOPRF framework based on effective isogeny group actions.
- Score: 2.7399382174132167
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Oblivious pseudorandom functions (OPRFs) allow a client to evaluate a keyed pseudorandom function on a private input without revealing that input to the server. In a threshold OPRF, the secret key is distributed among (n) servers so that any qualified set of at least (t) servers can complete an evaluation, while fewer than (t) shares reveal no information about the key. Existing isogeny-based threshold OPRFs, however, are primarily designed for static corruption models. If the same shares remain valid throughout the lifetime of the service, a mobile adversary can compromise different servers over time, accumulate (t) shares from the same sharing state, and eventually recover the master key. We introduce PIVOT (Proactive Isogeny-based Verifiable Oblivious Threshold PRF), a dealerless threshold VOPRF framework based on effective isogeny group actions. PIVOT periodically refreshes the server shares without changing the master key, public key, or previously generated OPRF outputs. The construction combines Shamir secret sharing, additively homomorphic coefficient commitments, sequential Lagrange-weighted group actions, and joint zero-knowledge relations that link certified shares to their corresponding isogeny actions. It also supports coordinated epoch transitions, publicly verifiable blame, secure erasure, and committee resharing under a possibly different threshold. We formalize the functionality of a long-lived proactive threshold VOPRF, prove the correctness of distributed key generation, threshold evaluation, proactive refresh, and committee resharing, and provide a simulation-based security analysis under the vectorization and one-more hidden-group- action assumptions. As an application, we describe a distributed private lookup service whose encrypted database remains valid across repeated share renewals and committee migrations.
Related papers
- PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning [19.825556330682783]
PRoVeFL is a novel, modular FL framework that is Privacy-preserving, Byzantine-Robust, and ensures Verifiable aggregation.<n> PRoVeFL employs multiple servers leveraging multi-key fully homomorphic encryption.<n> PRoVeFL is compatible with a wide range of state-of-the-art Byzantine-robust aggregation algorithms.
arXiv Detail & Related papers (2026-07-07T07:10:43Z) - DKVE: Decentralized Key Validation for End-to-End Encrypted Messaging [0.7978226788634308]
End-to-end encrypted messaging systems depend on authentic public key distribution to prevent man-in-the-middle (MitM) attacks.<n>Current solutions present a stark trade-off: out-of-band (OOB) verification provides strong security but lacks scalability for large contact lists.<n>We propose DKVE, a protocol that validates public keys through privacy-preserving cross-validation within users' social graphs.
arXiv Detail & Related papers (2026-06-25T00:43:59Z) - The Capacity of Information-Theoretic Secure Aggregation in Federated Learning [38.64656884737012]
We study the $T$-colluding information-theoretic secure aggregation problem with $N$ users.<n>Unlike prior work, we model key distribution through user-to-user communication.<n>We show that the optimal performance can be achieved using only pairwise shared keys.
arXiv Detail & Related papers (2026-06-05T13:53:27Z) - Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions [89.52532304099522]
Federated Learning (FL) enables clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy.<n> gradients and model updates can still leak sensitive information, while malicious servers may mount adversarial attacks such as Byzantine manipulation.<n>These vulnerabilities highlight the need to address differential privacy (DP) and Byzantine robustness within a unified framework.<n>We propose Byz-Clip21-SGD2M, a new algorithm that integrates robust aggregation with double momentum and carefully designed clipping.
arXiv Detail & Related papers (2026-03-24T17:39:09Z) - Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework [57.04850867402913]
Federated clustering addresses the challenge of extracting patterns from decentralized, unlabeled data.<n>We propose Structural Privacy-Preserving Federated Graph Clustering (SPP-FGC), a novel algorithm that innovatively leverages local structural graphs as the primary medium for privacy-preserving knowledge sharing.<n>Our framework achieves state-of-the-art performance, improving clustering accuracy by up to 10% (NMI) over federated baselines while maintaining provable privacy guarantees.
arXiv Detail & Related papers (2025-11-14T03:05:22Z) - Perfectly-Private Analog Secure Aggregation in Federated Learning [51.61616734974475]
In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model.<n>In this paper, a novel secure parameter aggregation method is proposed that employs the torus rather than a finite field.
arXiv Detail & Related papers (2025-09-10T15:22:40Z) - COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
arXiv Detail & Related papers (2025-06-25T07:04:49Z) - Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning [7.808916974942399]
heterogeneous edge devices produce diverse, non-independent, and identically distributed (non-IID) data.<n>We propose a novel representative-attention-based defense mechanism, named FeRA, to distinguish benign from malicious clients.<n>Our evaluation demonstrates FeRA's robustness across various FL scenarios, including challenging non-IID data distributions typical of edge devices.
arXiv Detail & Related papers (2025-05-15T13:44:32Z) - 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) - Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning [13.64339376830805]
We propose a more reliable and anonymously authenticated scheme called Chu-ko-nu for secure aggregation.
Chu-ko-nu breaks the probability P barrier by supplementing a redistribution process of secret key components.
It can support clients anonymously participating in FL training and enables the server to authenticate clients effectively in the presence of attacks.
arXiv Detail & Related papers (2024-02-23T05:50:43Z) - 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)
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.