Scalable Bias-Resistant Distributed Randomness

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Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on Device-to-Device (D2D) ecosystems (e.g., crowdsensing). Sophisticated applications, like cryptocurrencies, need distr ...

Online services have become ubiquitous in technological society, the global demand for which has driven enterprises to construct gigantic datacenters that run their software. Such facilities have also recently become a substrate for third-party organizatio ...

Tendermint is a deterministic consensus protocol and is one of the most mature implementations of its kind. This implementation is used as the core for building State Machine Replication (SMR) platforms with Byzantine fault-tolerant (BFT) guarantees. A not ...

The discretization of robust quadratic optimal control problems under uncertainty using the finite element method and the stochastic collocation method leads to large saddle-point systems, which are fully coupled across the random realizations. Despite its ...

Front-running, the practice of benefiting from advanced knowledge of pending transactions, has proliferated in the cryptocurrency space with the emergence of decentralized finance. Front-running causes devastating losses to honest participants-estimated at ...

This paper examines the minimization of the cost for an expected random production output, given an assembly of finished goods from two random inputs, matched in two categories. We describe the optimal input portfolio, first using the standard normal appro ...

An oblivious linear function evaluation protocol, or OLE, is a two-party protocol for the function f (x) = ax + b, where a sender inputs the field elements a, b, and a receiver inputs x and learns f (x). OLE can be used to build secret-shared multiplicatio ...

Blockchain based systems allow various kinds of financial transactions to be executed in a decentralized manner. However, these systems often rely on a trusted third party (oracle) to get correct information about the real-world events, which trigger the f ...

In this work, we tackle the problem of minimising the Conditional-Value-at-Risk (CVaR) of output quantities of complex differential models with random input data, using gradient-based approaches in combination with the Multi-Level Monte Carlo (MLMC) method ...

In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...

Scalable Bias-Resistant Distributed Randomness

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Toward Mobile Distributed Ledgers

Hardware and Software Support for RPC-Centric Server Architecture

Revisiting Tendermint: Design Tradeoffs, Accountability, and Practical Use

Preconditioners for robust optimal control problems under uncertainty

Flash Freezing Flash Boys: Countering Blockchain Front-Running

Optimal Matching of Random Parts

Efficient protocols for oblivious linear function evaluation from ring-LWE

Infochain: A Decentralized, Trustless and Transparent Oracle on Blockchain

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Quantization for Distributed Processing and Learning of Structured Data