We present novel Secure Multi-Party Computation (SMPC) protocols to perform Breadth-First-Searches (BFSs) and determine maximal flows on dense secret-shared graphs. In particular, we introduce a novel BFS protocol that requires only $\mathcal{O}(\log n)$ communication rounds on graphs with $n$ nodes, which is a big step from prior work that requires $\mathcal{O}(n \log n)$ rounds. This BFS protocol is then used in a maximal flow protocol based on the Edmonds-Karp algorithm, which requires...

Explainable AI (XAI) refers to the development of AI systems and machine learning models in a way that humans can understand, interpret and trust the predictions, decisions and outputs of these models. A common approach to explainability is feature importance, that is, determining which input features of the model have the most significant impact on the model prediction. Two major techniques for computing feature importance are LIME (Local Interpretable Model-agnostic Explanations) and...

We propose a novel protocol, Falkor, for secure aggregation for Federated Learning in the multi-server scenario based on masking of local models via a stream cipher based on AES in counter mode and accelerated by GPUs running on the aggregating servers. The protocol is resilient to client dropout and has reduced clients/servers communication cost by a factor equal to the number of aggregating servers (compared to the naïve baseline method). It scales simultaneously in the two major...

Secure multi-party computation (SMPC) protocols allow several parties that distrust each other to collectively compute a function on their inputs. In this paper, we introduce a protocol that lifts classical SMPC to quantum SMPC in a composably and statistically secure way, even for a single honest party. Unlike previous quantum SMPC protocols, our proposal only requires very limited quantum resources from all but one party; it suffices that the weak parties, i.e. the clients, are able to...

To date, ideal functionalities securely realized with secure multi-party computation (SMPC) mainly considers functions of the private inputs of a fixed number of a priori known parties. In this paper, we generalize these definitions such that protocols implementing online algorithms in a distributed fashion can be proven to be privacy-preserving. Online algorithms compute online functionalities that allow parties to arrive and leave over time, to provide multiple inputs and to obtain...

Post-quantum cryptography (PQC) is nowadays a very active research field [1]. We follow a non-standard way to achieve it, taking any common protocol and replacing arithmetic with GF(2^8) field operations, a procedure defined as R-Propping [2-7]. The resulting protocol security relies on the intractability of a generalized discrete log problem, combined with the power sets of algebraic ring extension tensors and resilience to quantum and algebraic attacks. Oblivious Transfer (OT) is a...

A Statistical Information Theoretic Secure (SITS) system utilizing the Chinese Remainder Theorem (CRT), coupled with Fully Homomorphic Encryption (FHE) for Distributed Communication-less Secure Multiparty Computation (DCLSMPC) of any Distributed Unknown Finite State Machine (DUFSM) is presented. Namely, secret shares of the input(s) and output(s) are passed to/from the computing parties, while there is no communication between them throughout the computation. We propose a novel approach of...

Systems with distributed trust have attracted growing research attention and seen increasing industry adoptions. In these systems, critical secrets are distributed across N servers, and computations are performed privately using secure multi-party computation (SMPC). Authentication for these distributed-trust systems faces two challenges. The first challenge is ease-of-use. Namely, how can an authentication protocol maintain its user experience without sacrificing security? To avoid a...

Oblivious transfer (OT) is a fundamental primitive in cryptography. Halevi-Kalai OT (Halevi, S. and Y. Kalai (2012), Journal of Cryptology 25(1)), which is based on smooth projective hash(SPH), is a famous and the most efficient framework for $1$-out-of-$2$ oblivious transfer ($\mbox{OT}^{2}_{1}$) against malicious adversaries in plain model. However, it does not provide simulation-based security. Thus, it is harder to use it as a building block in secure multiparty computation (SMPC)...

Multi-party fair exchange (MFE) and fair secure multi-party computation (fair SMPC) are is under-studied field of research, with practical importance. In particular, we consider MFE scenarios where at the end of the protocol, either every participant receives every other participant’s item, or no participant receives anything. We analyze the case where a trusted third party (TTP) is optimistically available, although we emphasize that the trust put on the TTP is only regarding the fairness,...

Computing aggregate statistics about user data is of vital importance for a variety of services and systems, but this practice has been shown to seriously undermine the privacy of users. Differential privacy has proved to be an effective tool to sanitize queries over a database, and various cryptographic protocols have been recently proposed to enforce differential privacy in a distributed setting, e.g., statical queries on sensitive data stored on the user’s side. The widespread deployment...