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Register a new userA First Look into DeFi Oracles
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Added by
Bowen Liu
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Recently emerging Decentralized Finance (DeFi) takes the promise of cryptocurrencies a step further, leveraging their decentralized networks to transform traditional financial products into trustless and transparent protocols that run without intermediaries. However, these protocols often require critical external information, like currency or commodity exchange rates, and in this respect they rely on special oracle nodes. In this paper, we present the first study of DeFi oracles deployed in practice. First, we investigate designs of mainstream DeFi platforms that rely on data from oracles. We find that these designs, surprisingly, position oracles as trusted parties with no or low accountability. Then, we present results of large-scale measurements of deployed oracles. We find and report that prices reported by oracles regularly deviate from current exchange rates, oracles are not free from operational issues, and their reports include anomalies. Finally, we compare the oracle designs and propose potential improvements.
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The decentralized and trustless nature of cryptocurrencies and blockchain technology leads to a shift in the digital world. The possibility to execute small programs, called smart contracts, on cryptocurrencies like Ethereum opened doors to countless new applications. One particular exciting use case is decentralized finance (DeFi), which aims to revolutionize traditional financial services by founding them on a decentralized infrastructure. We show the potential of DeFi by analyzing its advantages compared to traditional finance. Additionally, we survey the state-of-the-art of DeFi products and categorize existing services. Since DeFi is still in its infancy, there are countless hurdles for mass adoption. We discuss the most prominent challenges and point out possible solutions. Finally, we analyze the economics behind DeFi products. By carefully analyzing the state-of-the-art and discussing current challenges, we give a perspective on how the DeFi space might develop in the near future.
YouTube has become the second most popular website according to Alexa, and it represents an enticing platform for scammers to attract victims. Because of the computational difficulty of classifying multimedia, identifying scams on YouTube is more difficult than text-based media. As a consequence, the research community to-date has provided little insight into the prevalence, lifetime, and operational patterns of scammers on YouTube. In this short paper, we present a preliminary exploration of scam videos on YouTube. We begin by identifying 74 search queries likely to lead to scam videos based on the authors experience seeing scams during routine browsing. We then manually review and characterize the results to identify 668 scams in 3,700 videos. In a detailed analysis of our classifications and metadata, we find that these scam videos have a median lifetime of nearly nine months, and many rely on external websites for monetization. We also explore the potential of detecting scams from metadata alone, finding that metadata does not have enough predictive power to distinguish scams from legitimate videos. Our work demonstrates that scams are a real problem for YouTube users, motivating future work on this topic.
Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial system, is mushrooming. One year ago the total value locked in DeFi systems was approximately 700m USD, now, as of April 2021, it stands at around 51bn USD. The frenetic evolution of the ecosystem makes it challenging for newcomers to gain an understanding of its basic features. In this Systematization of Knowledge (SoK), we delineate the DeFi ecosystem along its principal axes. First, we provide an overview of the DeFi primitives. Second, we classify DeFi protocols according to the type of operation they provide. We then go on to consider in detail the technical and economic security of DeFi protocols, drawing particular attention to the issues that emerge specifically in the DeFi setting. Finally, we outline the open research challenges in the ecosystem.
As the COVID-19 pandemic emerged in early 2020, a number of malicious actors have started capitalizing the topic. Although a few media reports mentioned the existence of coronavirus-themed mobile malware, the research community lacks the understanding of the landscape of the coronavirus-themed mobile malware. In this paper, we present the first systematic study of coronavirus-themed Android malware. We first make efforts to create a daily growing COVID-19 themed mobile app dataset, which contains 4,322 COVID-19 themed apk samples (2,500 unique apps) and 611 potential malware samples (370 unique malicious apps) by the time of mid-November, 2020. We then present an analysis of them from multiple perspectives including trends and statistics, installation methods, malicious behaviors and malicious actors behind them. We observe that the COVID-19 themed apps as well as malicious ones began to flourish almost as soon as the pandemic broke out worldwide. Most malicious apps are camouflaged as benign apps using the same app identifiers (e.g., app name, package name and app icon). Their main purposes are either stealing users private information or making profit by using tricks like phishing and extortion. Furthermore, only a quarter of the COVID-19 malware creators are habitual developers who have been active for a long time, while 75% of them are newcomers in this pandemic. The malicious developers are mainly located in US, mostly targeting countries including English-speaking countries, China, Arabic countries and Europe. To facilitate future research, we have publicly released all the well-labelled COVID-19 themed apps (and malware) to the research community. Till now, over 30 research institutes around the world have requested our dataset for COVID-19 themed research.
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in datacenters. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL, in particular, is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and civil society for privacy protection. However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL. First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Our findings show that FL, despite being slower to converge in some cases, may result in a comparatively greener impact than a centralized equivalent setup. We performed extensive experiments across different types of datasets, settings, and various deep learning models with FL. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.
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