Private Aggregation with Custom Collusion Tolerance

While multiparty computations are becoming more and more efficient, their performance has not yet reached the required level for wide adoption. Nevertheless, many applications need this functionality, while others need it for simpler computations; operations such as multiplication or addition might be sufficient. In this work we extend the well-known multiparty computation protocol (MPC) for summation of Kurswave et al. More precisely, we introduce two extensions of the protocol one which bases its security on the Decisional Diffie-Hellman hypothesis and does not use pairings, and one that significantly reduces the pairings of the original. Both protocols are proven secure in the semi-honest model. Like the original, the protocols are entirely broadcast-based and self-bootstrapping, but provide a significant performance boost, allowing them to be adopted by devices with low processing power and can also be extended naturally to achieve \(t\)-privacy in the malicious model, while remaining practical. Finally, the protocols can further improve their performance if users decide to decrease their collusion tolerance.