For more than a decade now, radio frequency identification (RFID) technology has been quite effective in providing anti-counterfeits measures in the supply chain. However, the genuineness of RFID tags cannot be guaranteed in the post supply chain, since these tags can be rather easily cloned in the public space. In this paper, we propose a novel product ownership management system (POMS) of RFID-attached products for anti-counterfeits that can be used in the post supply chain. For this purpose, we leverage the idea of Bitcoin's blockchain that anyone can check the proof of possession of balance. With the proposed POMS, a customer can reject the purchase of counterfeits even with genuine RFID tag information, if the seller does not possess their ownership. We have implemented a proof-of-concept experimental system employing a blockchain-based decentralized application platform, Ethereum, and evaluated its cost performance. Results have shown that, typically, the cost of managing the ownership of a product with up to six transfers is less than U.S. $1.

The advent of federated learning (FL) has sparked a new paradigm of parallel and confidential decentralized machine learning (ML) with the potential of utilizing the computational power of a vast number of Internet of Things (IoT), mobile, and edge devices without data leaving the respective device, thus ensuring privacy by design. Yet, simple FL frameworks (FLFs) naively assume an honest central server and altruistic client participation. In order to scale this new paradigm beyond small groups of already entrusted entities toward mass adoption, FLFs must be: 1) truly decentralized and 2) incentivized to participants. This systematic literature review is the first to analyze FLFs that holistically apply both, the blockchain technology to decentralize the process and reward mechanisms to incentivize participation. 422 publications were retrieved by querying 12 major scientific databases. After a systematic filtering process, 40 articles remained for an in-depth examination following our five research questions. To ensure the correctness of our findings, we verified the examination results with the respective authors. Although having the potential to direct the future of distributed and secure artificial intelligence, none of the analyzed FLFs is production ready. The approaches vary heavily in terms of use cases, system design, solved issues, and thoroughness. We provide a systematic approach to classify and quantify differences between FLFs, expose limitations of current works and derive future directions for research in this novel domain.

Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as HYIP (High Yield Investment Programs). Although from the security forensic point of view it is very important to identify HYIP operators’ Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of HYIP operators’ Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators’ Bitcoin addresses from the Internet thus providing a large number of HYIPs’ samples. Secondly, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments, have shown that the proposed methodology achieves excellent performance, i.e. 95In order to further validate the proposed classifier’s ability to detect HYIP operators’ Bitcoin addresses, our designed classifier has been tested against a recently published list of HYIP addresses maintaining its excellent detection accuracy by achieving 93.75.

Private Ethereum blockchain-based systems are demanded in many industry sectors. However, the throughput performance of these systems does not meet their expectations. Many researchers have analyzed the performance of private blockchains, but their studies have failed to analyze root causes. In this paper, we perform a deep function-level bottleneck analysis for the private Ethereum blockchain. As the Ethereum client application is developed with golang, we leverage pprof, which is a resourceprofiling tool for golang, and custom golang functions to measure the time taken by functions. To easily configure parameters and conduct our test, we code a shell script that automates the building process of a private Ethereum blockchain with docker containers. We conducted a series of experiments and identified the bottleneck function that is called every time a transaction arrives at an Ethereum node. In addition, we also found that the multi-threading is not well utilized, meaning that there is much room for improvement.

Federated learning (FL) is a promising decentralized deep learning technique that allows users to collaboratively update models without sharing their own data. However, due to its decentralized nature, no one can monitor workers’ behavior, and they may thus deviate protocols (e.g., participating without updating any models). To solve this problem, many researchers have proposed blockchain-enabled FL to reward workers (or users) with cryptocurrencies to encourage workers to follow the protocols. However, there is a lack of theoretical discussions concerning how such rewards impact workers’ behavior and how much should be given to workers. In this paper, we propose a mechanism-design-oriented FL protocol on a public blockchain network. Mechanism design (MD) is often used to make a rule intended to achieve a specific goal. With MD in mind, we introduce the concept of competition into blockchain-based FL so that only workers who have contributed well can obtain rewards, which naturally prevents workers from deviating from the protocol. We then mathematically answer the following questions with contest theory, a novel field of study in economics: i) What behavior will workers take?; ii) how much effort should workers exert to maximize their profits?; iii) how many workers should be rewarded?; and iv) what is the best proportion for reward distribution?.

Demands for vital sign monitoring are increasing in the field of health care. In particular, the RRI (R-R Interval) estimation has been studied extensively, since the RRI variation is highly related with the stress of a subject. Various Doppler sensor based-heartbeat detection methods have been proposed so far, thanks to non-contact and non-invasive features of a Doppler sensor. In our previous research, we have proposed a Doppler sensor-based RRI estimation method by a spectrogram. In this method, the spectra due to heartbeats are integrated on a spectrogram, and then the RRI is estimated by detecting the peaks of the integrated spectrum. However, the undesired peaks sometimes appear even in the situation where a subject sits still. In this paper, as the extended version of our previous method, we propose a Doppler sensor- based RRI estimation method leveraging the accurate peak detection. In the proposed method, to prevent the incorrect peak detection, the peaks due to heartbeats are detected using some peaks before and after the investigated peak. Through the experiments on 10 subjects in the cases where a subject was sitting still, typing, and speaking, we confirmed that the proposed method improved our previous and state-of-the-art ones by the RMSE (Root Mean Squared Error) of the RRI. Furthermore, based on the estimated RRI, we calculated the stress index LF/HF (Low-Frequency/High-Frequency), which is one of the useful indices to evaluate the stress of a subject. As a result, our proposed method outperformed the other ones by the RE (Relative Error) of the LF/HF.