Compact Multi-Signatures for Smaller Blockchains

We construct new multi-signature schemes that provide new functionality. Our schemes are designed to reduce the size of the Bitcoin blockchain, but are useful in many other settings where multi-signatures are needed. All our constructions support both signature compression and public-key aggregation. Hence, to verify that a number of parties signed a common message m, the verifier only needs a short multi-signature, a short aggregation of their public keys, and the message m. We give new constructions that are derived from Schnorr signatures and from BLS signatures. Our constructions are in the plain public key model, meaning that users do not need to prove knowledge or possession of their secret key. In addition, we construct the first short accountable-subgroup multi-signature (ASM) scheme. An ASM scheme enables any subset S of a set of n parties to sign a message m so that a valid signature discloses which subset generated the signature (hence the subset S is accountable for signing m). We construct the first ASM scheme where signature size is only O(k) bits over the description of S, where k is the security parameter. Similarly, the aggregate public key is only O(k) bits, independent of n. The signing process is non-interactive. Our ASM scheme is very practical and well suited for compressing the data needed to spend funds from a t-of-n Multisig Bitcoin address, for any (polynomial size) t and n

Blockchain-based Security Framework for Critical Industry 4.0 Cyber-physical System

There has been an intense concern for security alternatives because of the recent rise of cyber attacks, mainly targeting critical systems such as industry, medical, or energy ecosystem. Though the latest industry infrastructures largely depend on AI-driven maintenance, the prediction based on corrupted data undoubtedly results in loss of life and capital. Admittedly, an inadequate data-protection mechanism can readily challenge the security and reliability of the network. The shortcomings of the conventional cloud or trusted certificate-driven techniques have motivated us to exhibit a unique Blockchain-based framework for a secure and efficient industry 4.0 system. The demonstrated framework obviates the long-established certificate authority after enhancing the consortium Blockchain that reduces the data processing delay, and increases cost-effective throughput. Nonetheless, the distributed industry 4.0 security model entails cooperative trust than depending on a single party, which in essence indulges the costs and threat of the single point of failure. Therefore, multi-signature technique of the proposed framework accomplishes the multi-party authentication, which confirms its applicability for the real-time and collaborative cyber-physical system.Comment: 07 Pages, 4 Figures, IEEE Communication Magazin

Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning

Federated learning introduces a novel approach to training machine learning (ML) models on distributed data while preserving user's data privacy. This is done by distributing the model to clients to perform training on their local data and computing the final model at a central server. To prevent any data leakage from the local model updates, various works with focus on secure aggregation for privacy preserving federated learning have been proposed. Despite their merits, most of the existing protocols still incur high communication and computation overhead on the participating entities and might not be optimized to efficiently handle the large update vectors for ML models. In this paper, we present E-seaML, a novel secure aggregation protocol with high communication and computation efficiency. E-seaML only requires one round of communication in the aggregation phase and it is up to 318x and 1224x faster for the user and the server (respectively) as compared to its most efficient counterpart. E-seaML also allows for efficiently verifying the integrity of the final model by allowing the aggregation server to generate a proof of honest aggregation for the participating users. This high efficiency and versatility is achieved by extending (and weakening) the assumption of the existing works on the set of honest parties (i.e., users) to a set of assisting nodes. Therefore, we assume a set of assisting nodes which assist the aggregation server in the aggregation process. We also discuss, given the minimal computation and communication overhead on the assisting nodes, how one could assume a set of rotating users to as assisting nodes in each iteration. We provide the open-sourced implementation of E-seaML for public verifiability and testing