Paper 2026/1033

A New Construction Method for More Efficient Quadratic One-Time Noisy Multi-Client Functional Encryption Schemes

Abstract

We introduce a new construction method for one-time multi-client functional encryption schemes that support noisy quadratic functions, are resistant against corruption and allow for labels. Such schemes can be used as building blocks in many practical applications, e.g., privacy preserving machine learning on arbitrarily split data. In contrast to earlier constructions, ours uses a different structural design that allows to make use of less complex, hence more efficient building blocks. The security of our construction relies solely on its underlying building blocks and no additional hardness assumptions, making it more generic than related work. More specifically, the construction itself does not rely on structures given by bilinear groups. We present a concrete instantiation, dubbed QUILT, and show in a series of experiments that it outperforms existing comparable schemes by far. For example, in the case of private logistic regression training, using QUILT yields a speed-up of 4.8x to 6.8x. Moreover, in contrast to these schemes, our construction allows for the use of labels. This weakens the one-time restriction, since multiple encryptions are possible, if each ciphertext is tied to a different label.