Practical and Provably Secure Distributed Aggregation: Verifiable Additive Homomorphic Secret Sharing
Journal article, 2020

Often clients (e.g., sensors, organizations) need to outsource joint computations that are based on some joint inputs to external untrusted servers. These computations often rely on the aggregation of data collected from multiple clients, while the clients want to guarantee that the results are correct and, thus, an output that can be publicly verified is required. However, important security and privacy challenges are raised, since clients may hold sensitive information. In this paper, we propose an approach, called verifiable additive homomorphic secret sharing (VAHSS), to achieve practical and provably secure aggregation of data, while allowing for the clients to protect their secret data and providing public verifiability i.e., everyone should be able to verify the correctness of the computed result.
We propose three VAHSS constructions by combining an additive homomorphic secret sharing (HSS) scheme, for computing the sum of the clients' secret inputs, and three different methods for achieving public verifiability, namely: (i) homomorphic collision-resistant hash functions; (ii) linear homomorphic signatures; as well as (iii) a threshold RSA signature scheme. In all three constructions, we provide a detailed correctness, security, and verifiability analysis and detailed experimental evaluations. Our results demonstrate the efficiency of our proposed constructions, especially from the client side.

homomorphic secret sharing

verifiable computation

public verifiability

function secret sharing


Georgia Tsaloli

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Gustavo Souza Banegas

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Aikaterini Mitrokotsa

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)


2410-387X (eISSN)

Vol. 4 3 25

Subject Categories

Media Engineering

Computer Science

Computer Vision and Robotics (Autonomous Systems)



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