Gustavo Camilo

Byzantine fault-tolerant (BFT) state machine replication (SMR) protocols form the basis of modern blockchains as they maintain a consistent state across all blockchain nodes while tolerating a bounded number of Byzantine faults. We analyze BFT SMR in the excessive fault setting where the actual number of Byzantine faults surpasses a protocol's tolerance.

We start by devising the very first repair algorithm for linearly chained and quorum-based partially synchronous SMR to recover from faulty states caused by excessive faults. Such a procedure can be realized using any commission fault detection module -- an algorithm that identifies the faulty replicas without falsely locating any correct replica. We achieve this with a slightly weaker liveness guarantee, as the original security notion is impossible to satisfy given excessive faults.

We implement recoverable HotStuff in Rust. The throughput resumes to the normal level (without excessive faults) after recovery routines terminate for replicas and is slightly reduced by for replicas. On average, it increases the latency by for replicas.

Aside from adopting existing detection modules, we also establish the sufficient condition for a general BFT SMR protocol to allow for complete and sound fault detection when up to Byzantine replicas (out of total replicas) attack safety. We start by providing the first closed-box fault detection algorithm for any SMR protocol without any extra rounds of communication. We then describe open-box instantiations of our fault detection routines in Tendermint and Hotstuff, further reducing the overhead, both asymptotically and concretely.

Every citizen has the right to privacy and, therefore, the right to control their personal information, deciding to whom, when, and where their information is available. This paper proposes a secure, agile, and effective system for a distributed, automatic, and transparent data trading between domains using blockchain, smart contracts, trust, and reputation. We develop and implement a prototype of a trust and reputation system based on real-life interactions. The results show that the proposed system provides security and privacy in a quick and distributed way, performing hundreds of transactions per second, and effectively punishing malicious behavior.

The effectiveness of machine learning systems depends heavily on the relevance of the training data. Usually, the collected data is sensitive and private because it comes from devices and sensors used in people’s daily lives. The General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in California, and China’s Cybersecurity Law put the current approach at risk, as it prohibits centralized remote processing of sensitive data collected in a distributed manner. This paper proposes a distributed machine learning system based on local random forest algorithms created with shared decision trees through the blockchain. The results show that the proposed approach equals or exceeds the results obtained with the use of random forests with only local data. Furthermore, the proposal increases the detection of new attacks when the domains have different threat distributions.