Contextualizing Alternative Models of Secret Sharing

A secret sharing scheme is a means of distributing information to a set of players such that any authorized subset of players can recover a secret and any unauthorized subset does not learn any information about the secret. In over forty years of research in secret sharing, there has been an emergence of new models and extended capabilities of secret sharing schemes. In this thesis, we study various models of secret sharing and present them in a consistent manner to provide context for each definition. We discuss extended capabilities of secret sharing schemes, including a comparison of methods for updating secrets via local computations on shares and an analysis of approaches to reproducing/repairing shares. We present an analysis of alternative adversarial settings which have been considered in the area of secret sharing. In this work, we present a formalization of a deniability property which is inherent to some classical secret sharing schemes. We provide new, game-based definitions for different notions of verifiability and robustness. By using consistent terminology and similar game-based definitions, we are able to demystify the subtle differences in each notion raised in the literature

On the Non-malleability of the Fiat-Shamir Transform

The Fiat-Shamir transform is a well studied paradigm for removing interaction from public-coin protocols. We investigate whether the resulting non-interactive zero-knowledge (NIZK) proof systems also exhibit non-malleability properties that have up to now only been studied for NIZK proof systems in the common reference string model: first, we formally define simulation soundness and a weak form of simulation extraction in the random oracle model (ROM). Second, we show that in the ROM the Fiat-Shamir transform meets these properties under lenient conditions. A consequence of our result is that, in the ROM, we obtain truly efficient non malleable NIZK proof systems essentially for free. Our definitions are sufficient for instantiating the Naor-Yung paradigm for CCA2-secure encryption, as well as a generic construction for signature schemes from hard relations and simulation-extractable NIZK proof systems. These two constructions are interesting as the former preserves both the leakage resilience and key-dependent message security of the underlying CPA-secure encryption scheme, while the latter lifts the leakage resilience of the hard relation to the leakage resilience of the resulting signature scheme

A Survey of Leakage-Resilient Cryptography

In the past 15 years, cryptography has made considerable progress in expanding the adversarial attack model to cover side-channel attacks, and has built schemes to provably defend against some of them. This survey covers the main models and results in this so-called leakage-resilient cryptography

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users. In particular, the overhead of the state-of-the-art protocols for secure model aggregation grows quadratically with the number of users. In this paper, we propose the first secure aggregation framework, named Turbo-Aggregate, that in a network with $N$ users achieves a secure aggregation overhead of $O(N\log{N})$, as opposed to $O(N^2)$, while tolerating up to a user dropout rate of $50\%$. Turbo-Aggregate employs a multi-group circular strategy for efficient model aggregation, and leverages additive secret sharing and novel coding techniques for injecting aggregation redundancy in order to handle user dropouts while guaranteeing user privacy. We experimentally demonstrate that Turbo-Aggregate achieves a total running time that grows almost linear in the number of users, and provides up to $40\times$ speedup over the state-of-the-art protocols with up to $N=200$ users. Our experiments also demonstrate the impact of model size and bandwidth on the performance of Turbo-Aggregate