Paper 2026/1370

smklhs: Succinct Multi key Linearly Homomorphic Signatures for Certified Statistics

Abstract

We study the problem of certifying statistical claims over datasets contributed by multiple independent sources. In this setting, an untrusted server aggregates signed data records and publishes claims such as sums, averages, or rates, while any third party can verify that these claims are correct with respect to the authenticated input data, without needing access to the underlying records. A central challenge is to achieve public verifiability without requiring trust in the aggregator, while keeping both the proof size and the verification cost small enough for practical deployment. This problem is motivated by applications in which reliable and scalable certification of published statistics is essential, including official health and demographic reporting. In this work, we present smklhs, a multi-key linearly homomorphic signature scheme for this setting. Compared to the state of the art, smklhs is the first practical construction to enjoy evaluated signatures of size logarithmic in the number of distinct signers involved in the computation, and else independent on the total number of input messages. We prove smklhs secure against fully adaptive adversaries in the random oracle and algebraic group models, under well-studied hardness assumptions in bilinear groups. We implement our scheme using the high-performance pairing library RELIC and compare it with prior work. To demonstrate practicality, we consider a case study on authenticated mortality statistics related to the impact of COVID-19 in Spain. At the 128-bit security level, our experiments show that an authenticated claim covering a 180-day nationwide dataset with over 300,000 signed records generated by 190 distinct signers can be verified in approximately 22 seconds on a commodity desktop machine. These results indicate that our approach is fast, lightweight, and practical for real-world deployment.