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

Nomadic: Normalising Maliciously-Secure Distance with Cosine Similarity for Two-Party Biometric Authentication

Computing the distance between two non-normalized vectors $\mathbfit{x}$ and $\mathbfit{y}$, represented by $\Delta(\mathbfit{x},\mathbfit{y})$ and comparing it to a predefined public threshold $\tau$ is an essential functionality used in privacy-sensitive applications such as biometric authentication, identification, machine learning algorithms ({\em e.g.,} linear regression, k-nearest neighbors, etc.), and typo-tolerant password-based authentication.

Jul 1, 2024

Efficient Three-party Boolean-to-Arithmetic Share Conversion

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Aug 1, 2023