TACITA: Threshold Aggregation without Client Interaction

Secure aggregation enables a central server to compute the sum of client inputs without learning any individual input, even in the presence of dropouts or partial participation. This primitive is fundamental to privacy-preserving applications such as federated learning, where clients collaboratively train models without revealing raw data. We present a new secure aggregation protocol, TACITA, in the single-server setting that satisfies four critical properties simultaneously: (1) one-shot communication from clients with no per-instance setup, (2) input-soundness, i.e. the server cannot manipulate the ciphertexts, (3) constant-size communication per client, independent of the number of participants per-instance, and (4) robustness to client dropouts Previous works on secure aggregation -Willow and OPA (CRYPTO'25) -achieve one-shot communication but differ in their treatment of input soundness. Willow does not provide input soundness, while OPA achieves it under the assumption of a public-key infrastructure (PKI) via signatures. In the absence of input soundness, secure aggregation protocols cannot guarantee full privacy. In contrast, we achieve full privacy at the cost of assuming a PKI. Specifically, TACITA relies on a novel cryptographic primitive we introduce and realize: succinct multi-key linearly homomorphic threshold signatures (MKLHTS), which enables verifiable aggregation of client-signed inputs with constant-size signatures. To encrypt client inputs, we adapt the Silent Threshold Encryption (STE) scheme of Garg et al. (CRYPTO 2024) to support ciphertext-specific decryption and additive homomorphism. We formally prove security in the Universal Composability framework and demonstrate practicality through an open-source proof-of-concept implementation, showing our protocol achieves scalability without sacrificing efficiency or requiring new trust assumptions.