João M. C. Sousa

Alysson Bessani, Eduardo Alchieri, João Sousa et al.

The popularization of blockchains leads to a resurgence of interest in Byzantine Fault-Tolerant (BFT) state machine replication protocols. However, much of the work on this topic focuses on the underlying consensus protocols, with emphasis on their lack of scalability, leaving other subtle limitations unaddressed. These limitations are related to the effects of maintaining a durable blockchain instead of a write-ahead log and the requirement for reconfiguring the set of replicas in a decentralized way. We demonstrate these limitations using a digital coin blockchain application and BFT-SMaRt, a popular BFT replication library. We show how they can be addressed both at a conceptual level, in a protocol-agnostic way, and by implementing SMaRtChain, a blockchain platform based on BFT-SMaRt. SMaRtChain improves the performance of our digital coin application by a factor of eight when compared with a naive implementation on top of BFT-SMaRt. Moreover, SMaRtChain achieves a throughput $8\times$ and $33\times$ better than Tendermint and Hyperledger Fabric, respectively, when ensuring strong durability on its blockchain.

David Froelicher, Juan R. Troncoso-Pastoriza, Joao Sa Sousa et al.

Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditional consent or is often simply forbidden for privacy and security reasons. In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets. Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evaluation of machine-learning models on sensitive and distributed data. To ensure data confidentiality and the privacy of the data providers, Drynx combines interactive protocols, homomorphic encryption, zero-knowledge proofs of correctness, and differential privacy. It enables an efficient and decentralized verification of the input data and of all the system's computations thus provides auditability in a strong adversarial model in which no entity has to be individually trusted. Drynx is highly modular, dynamic and parallelizable. Our evaluation shows that it enables the training of a logistic regression model on a dataset (12 features and 600,000 records) distributed among 12 data providers in less than 2 seconds. The computations are distributed among 6 computing nodes, and Drynx enables the verification of the query execution's correctness in less than 22 seconds.

João Sousa, Alysson Bessani, Marko Vukolić

Hyperledger Fabric (HLF) is a flexible permissioned blockchain platform designed for business applications beyond the basic digital coin addressed by Bitcoin and other existing networks. A key property of HLF is its extensibility, and in particular the support for multiple ordering services for building the blockchain. Nonetheless, the version 1.0 was launched in early 2017 without an implementation of a Byzantine fault-tolerant (BFT) ordering service. To overcome this limitation, we designed, implemented, and evaluated a BFT ordering service for HLF on top of the BFT-SMaRt state machine replication/consensus library, implementing also optimizations for wide-area deployment. Our results show that HLF with our ordering service can achieve up to ten thousand transactions per second and write a transaction irrevocably in the blockchain in half a second, even with peers spread in different continents.