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1. Responsible vulnerability disclosure for cryptocurrencies

   Boehme, R; Eckey, L; Moore, T; Narula, N; Ruffing, T; Zohar, A (October 2020, Communications of the ACM)

   Interest in cryptocurrencies has surged in recent years. Today thousands of currencies are in circulation, collectively worth hundreds of billions of dollars. Software vulnerabilities have also proliferated, which poses new and unique challenges to the ecosystem as it has developed. This review article explains what is different about vulnerabilities and responsible disclosure in cryptocurrencies, identifying key problems and opportunities for research and development. Selected case studies of vulnerability disclosures are presented. We draw lessons and pose open questions that can inform the responsible disclosure debate in cryptocurrencies and beyond. 

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2. On the role of local blockchain network features in cryptocurrency price formation

   https://doi.org/10.1002/cjs.11547  

   Dey, Asim_K; Akcora, Cuneyt_G; Gel, Yulia_R; Kantarcioglu, Murat (March 2020, Canadian Journal of Statistics)

   <italic>Abstract</italic> Cryptocurrencies and the underpinning blockchain technology have gained unprecedented public attention recently. In contrast to fiat currencies, transactions of cryptocurrencies, such as Bitcoin and Litecoin, are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions allows us to create a complex network of financial interactions that can be used to study not only the blockchain graph, but also the relationship between various blockchain network features and cryptocurrency risk investment. We introduce a novel concept of chainlets, or blockchain motifs, to utilize this information. Chainlets allow us to evaluate the role of local topological structure of the blockchain on the joint Bitcoin and Litecoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on cryptocurrency price and investment risk. More generally, while statistical aspects of blockchain data analytics remain virtually unexplored, the paper aims to highlight various emerging theoretical, methodological and applied research challenges of blockchain data analysis that will be of interest to the broad statistical community.The Canadian Journal of Statistics48: 561–581; 2020 © 2020 Statistical Society of Canada 

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3. An Open-Source Workflow for Spatiotemporal Studies with COVID-19 as an Example

   https://doi.org/10.3390/ijgi11010013  

   Malarvizhi, Anusha Srirenganathan; Liu, Qian; Sha, Dexuan; Lan, Hai; Yang, Chaowei (January 2022, ISPRS International Journal of Geo-Information)

   Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code, and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository. 

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4. Diversifying the genomic data science research community

   https://doi.org/10.1101/gr.276496.121  

   The Genomic Data Science Community Network (July 2022, Genome Research)

   Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support. 

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5. Multilayered Defense-in-Depth Architecture for Cryptocurrency Wallet

   https://doi.org/10.1109/ICCC51575.2020.9345013  

   Rezaeighaleh, Hossein; Zou, Cliff C. (December 2020, 2020 IEEE 6th International Conference on Computer and Communications (ICCC))

   null (Ed.)

   A significant challenge in blockchain and cryptocurrencies is protecting private keys from potential hackers because nobody can rollback a transaction made with a stolen key once the blockchain network confirms the transaction. The technical solution to protect private keys is cryptocurrency wallets, a piece of software, hardware, or a combination of them to manage the keys. In this paper, we propose a multilayered architecture for cryptocurrency wallets based on a Defense-in-Depth strategy to protect private keys with a balance between convenience and security. The user protects the private keys in three restricted layers with different protection mechanisms. So, a single breach cannot threaten the entire fund, and it saves time for the user to respond. We implement a proof-of-concept of our proposed architecture on both a smart card hardware wallet and an Android smartphone wallet with no performance penalty. Furthermore, we analyze the security of our proposed architecture with two adversary models. 

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