Analysis and Application of Verifiable Computation Techniques in Blockchain Systems for the Energy Sector

The energy system is becoming increasingly decentralized. This development requires integrating and coordinating a rising number of actors and small units in a complex system. Blockchain could provide a base infrastructure for new tools and platforms that address these tasks in various aspects—ranging from dispatch optimization or dynamic load adaption to (local) market mechanisms. Many of these applications are currently in development and subject to research projects. In decentralized energy markets especially, the optimized allocation of energy products demands complex computation. Combining these with distributed ledger technologies leads to bottlenecks and challenges regarding privacy requirements and performance due to limited storage and computational resources. Verifiable computation techniques promise a solution to these issues. This paper presents an overview of verifiable computation technologies, including trusted oracles, zkSNARKs, and multi-party computation. We further analyze their application in blockchain environments with a focus on energy-related applications. Applied to a distinct optimization problem of renewable energy certificates, we have evaluated these solution approaches and finally demonstrate an implementation of a Simplex-Optimization using zkSNARKs as a case study. We conclude with an assessment of the applicability of the described verifiable computation techniques and address limitations for large-scale deployment, followed by an outlook on current development trends.

SoK: Privacy-Preserving Computation Techniques for Deep Learning

Deep Learning (DL) is a powerful solution for complex problems in many disciplines such as finance, medical research, or social sciences. Due to the high computational cost of DL algorithms, data scientists often rely upon Machine Learning as a Service (MLaaS) to outsource the computation onto third-party servers. However, outsourcing the computation raises privacy concerns when dealing with sensitive information, e.g., health or financial records. Also, privacy regulations like the European GDPR limit the collection, distribution, and use of such sensitive data. Recent advances in privacy-preserving computation techniques (i.e., Homomorphic Encryption and Secure Multiparty Computation) have enabled DL training and inference over protected data. However, these techniques are still immature and difficult to deploy in practical scenarios. In this work, we review the evolution of the adaptation of privacy-preserving computation techniques onto DL, to understand the gap between research proposals and practical applications. We highlight the relative advantages and disadvantages, considering aspects such as efficiency shortcomings, reproducibility issues due to the lack of standard tools and programming interfaces, or lack of integration with DL frameworks commonly used by the data science community.

A Review of Alternative Procedures to the Bond Ball Mill Standard Grindability Test

Over the years, alternative procedures to the Bond grindability test have been proposed aiming to avoid the need for the standard mill or to reduce and simplify the grinding procedure. Some of them use the standard mill, while others are based on a non-standard mill or computation techniques. Therefore, papers targeting to propose a better alternative claim to improve validity, to reduce test duration, or to propose simpler and faster alternative methods for determining the Bond work index (wi). In this review paper, a compilation and critical analysis of selected proposals is performed, concluding that some of the short procedures could be useful for control purposes, while the simulation-based procedures could be interesting within a process digitalisation strategy.

Preplay Communication in Multi-Player Sequential Games: An Overview of Recent Results

The computational study of game-theoretic solution concepts is fundamental to describe the optimal behavior of rational agents interacting in a strategic setting, and to predict the most likely outcome of a game. Equilibrium computation techniques have been applied to numerous real-world problems. Among other applications, they are the key building block of the best poker-playing AI agents [5, 6, 27], and have been applied to physical and cybersecurity problems (see, e.g., [18, 20, 21, 30–32]).