An increasing number of open-source libraries promise to bring differential privacy to practice, even for non-experts. This paper studies five libraries that offer differentially private analytics: Google DP, SmartNoise, diffprivlib, diffpriv, and Chorus. We compare these libraries qualitatively (capabilities, features, and maturity) and quantitatively (utility and scalability) across four analytics queries (count, sum, mean, and variance) executed on synthetic and realworld datasets. We conclude that these libraries provide similar utility (except in some notable scenarios). However, there are significant differences in the features provided, and we find that no single library excels in all areas. Based on our results, we provide guidance for practitioners to help in choosing a suitable library, guidance for library designers to enhance their software, and guidance for researchers on open challenges in differential privacy tools for non-experts.

Modelling functionalities of the brain in human-robot interaction contexts requires a real-time understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how they interact all together to accomplish complex behavioural tasks while interacting with the environment. Human brains are very efficient as they process the information using event-based impulses also known as spikes, which make living creatures very efficient and able to outperform current mainstream robotic systems in almost every task that requires real-time interaction. In recent years, combined efforts by neuroscientists, biologists, computer scientists and engineers make it possible to design biologically realistic hardware and models that can endow the robots with the required human-like processing capability based on neuromorphic computing and Spiking Neural Network (SNN). However, while some attempts have been made, a comprehensive combination of neuromorphic computing and robotics is still missing. In this article, we present a systematic review of neuromorphic computing applications for socially interactive robotics. We first introduce the basic principles, models and architectures of neuromorphic computation. The remaining articles are classified according to the applications they focus on. Finally, we identify the potential research topics for fully integrated socially interactive neuromorphic robots.

Blockchain is a decentralized transaction and data management technology. It was developed for the world's first cryptocurrency known as Bitcoin in 2008. The reason behind its popularity was its properties which provide pseudonymity, security, and data integrity without third-party intervention. Initially, most of the researches were focused on the Bitcoin system and its limitation, but later other applications of Blockchain e.g. smart contracts and licensing [1] also got famous. Blockchain technology has the potential to change the way how transactions are conducted in daily life. It is not limited to cryptocurrencies but could be possibly applied in various environments where any forms of transactions are done. This article presents a comprehensive overview of Blockchain technology, its development, applications, security issues, and their countermeasures. In particular, the security towards illegal data insertion and the countermeasures is focused. Our analysis of countermeasures of illegal data insertion can be combined for increased efficiency. After the introduction of the Blockchain and consensus algorithm, some famous Blockchain applications and expected future of Blockchain are deliberated. Then, the technical challenges of Blockchain are discussed, in which the main focus here is on the security and the data insertion in Blockchain. The review of the possible countermeasures to overcome the security issues related to data insertion are elaborated.

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