Efficient Two-Party Secure Aggregation via Incremental Distributed Point Function

In this paper, we investigate whether I-DPF can be used to improve the efficiency of secure two-party computation (2PC) with an emphasis on computing the maximum value and the k-th (with $k$ unknown to the computing parties) ranked value from a list of secret inputs. Our answer is affirmative, and we propose novel secure 2PC protocols that use I-DPF as a building block, resulting in significant efficiency gains compared to the state-of-the-art. More precisely, our contributions are (i) We present two new secure computation frameworks that efficiently compute secure aggregation statistics bit-wisely or batch-wisely; (ii) we propose novel protocols to compute the maximum value, the $k$-th ranked value from a list of secret inputs; (iii) we provide variations of the proposed protocols that can perform batch computations and thus provide further efficiency improvements; and (iv) we provide an extensive performance evaluation for all proposed protocols.