Efficient Two-Party Secure Aggregation via Incremental Distributed Point Function
Computing the maximum from a list of secret inputs is a widely-used functionality that is employed either indirectly as a building block in secure computation frameworks, such as ABY (NDSS'15) or directly used in multiple applications that solve optimisation problems, such as secure machine learning or secure aggregation statistics. {\em Incremental distributed point function} (I-DPF) is a powerful primitive (IEEE S\&P'21) that significantly reduces the client-to-server communication and are employed to efficiently and securely compute aggregation statistics. Our protocols have a communication complexity that is independent of the number of secret inputs and linear to the length of the secret input domain. Our experimental results show enhanced efficiency over state-of-the-art solutions, particularly notable when handling large-scale inputs. For instance, in scenarios involving an input set of five million elements with an input domain size of 31 bits, our protocol \(\Pi_{\mathsf{Max}}\) achieves an $18\%$ reduction in online execution time and a $67\%$ decrease in communication volume compared to the most efficient existing solution.
Jul 2, 2024
