Secure aggregation of sufficiently many private inputs

Secure aggregation of distributed inputs is a well-studied problem. In this study, anonymity of inputs is achieved by assuring a minimal quota before publishing the outcome. We design and implement an efficient cryptographic protocol that mitigates the most important security risks and show its application in the cyber threat intelligence (CTI) domain. Our approach allows for generic aggregation and quota functions. With 20 inputs from different parties, we can do three secure and anonymous aggregations per second, and in a CTI community of 100 partners, 10, 000 aggregations could be performed during one night.

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Publication Details

Journal
Frontiers in Big Data
Published
2025-09-10
DOI
https://doi.org/10.3389/fdata.2025.1638307
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00

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article

Secure aggregation of sufficiently many private inputs

Gabriele Spini, Thijs Veugen, Frank Müller
Frontiers in Big Data
Privacy-Preserving Technologies in Data
article

Secure aggregation of sufficiently many private inputs

Gabriele Spini, Thijs Veugen, Frank Müller
article en

Abstract

Secure aggregation of distributed inputs is a well-studied problem. In this study, anonymity of inputs is achieved by assuring a minimal quota before publishing the outcome. We design and implement an efficient cryptographic protocol that mitigates the most important security risks and show its application in the cyber threat intelligence (CTI) domain. Our approach allows for generic aggregation and quota functions. With 20 inputs from different parties, we can do three secure and anonymous aggregations per second, and in a CTI community of 100 partners, 10, 000 aggregations could be performed during one night.

Frontiers in Big DataVol. 8
AIT Austrian Institute of Technology GmbH (AT), The Hague Centre for Strategic Studies (NL), University of Twente (NL)
European Commission
Openalex Percentile: Top 87%
Privacy-Preserving Technologies in Data
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Secure aggregation of sufficiently many private inputs — Gabriele Spini, Thijs Veugen, et al. · Frontiers in Big Data (2025) | TGRS Research Map | TGRS