Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an owning entity’s real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for de-anonymizing the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilized a sample of 957 entities (with ≈385 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 12 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm with default parameters, we achieve a mean cross-validation accuracy of 80.42% and F1-score of ≈79.64%. We show two examples, one where we predict on a set of 22 clusters that are suspected to be related to cybercriminal activities, and another where we classify 153,293 clusters to provide an estimation of the activity on the Bitcoin ecosystem. We discuss the potential applications of our method for organizational regulation and compliance, societal implications, outline study limitations, and propose future research directions. A prototype implementation of our method for organizational use is included in the appendix.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Huge amount of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the healthcare ecosystem. Currently, most of these data are siloed and fragmented in different healthcare systems or public and private databases. It prevents the fulfillment of intelligent healthcare inspired by these big data. Security/privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, Distributed Ledger Technologies (DLT) provide reliable solutions, such as blockchain, Ethereum and IOTA Tangle, to facilitate the healthcare data sharing. Objective: Develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resist, high-scalable and granular controllable health data exchange. Build a prototype and conduct experiments to verify the feasibility of the proposed solution. Methods: The health-related data are generated by two types of IoT devices, wearable/mobile devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA’s distributed ledger, the Tangle, which is a directed acyclic graph (DAG). Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree (MHT) is used for data encryption and verification. Results: A prototype system was built according to the proposed solution, using smartwatches, smartphones, several air sensors and Raspberry Pi. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and control its access management in a fully decentralized manner during data sharing. Users can easily authorize or revoke the access to their data to different subscribers. Conclusions: DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability and flexibility in data access management.

Current analytical approaches in computational social science can be characterized by four dominant paradigms: text analysis (information extraction and classification), social network analysis (graph theory), social complexity analysis (complex systems science), and social simulations (cellular automata and agent-based modeling). However, when it comes to organizational and societal units of analysis, there exists no approach to conceptualize, model, analyze, explain, and predict social media interactions as individuals' associations with ideas, values, identities, and so on. To address this limitation, based on the sociology of associations and the mathematics of set theory, this paper presents a new approach to big data analytics called social set analysis. Social set analysis consists of a generative framework for the philosophies of computational social science, theory of social data, conceptual and formal models of social data, and an analytical framework for combining big social data sets with organizational and societal data sets. Three empirical studies of big social data are presented to illustrate and demonstrate social set analysis in terms of fuzzy set-theoretical sentiment analysis, crisp set-theoretical interaction analysis, and event-studies-oriented set-theoretical visualizations. Implications for big data analytics, current limitations of the set-theoretical approach, and future directions are outlined.

In recent years there has been growing research attention and practitioner interest in exploring the suitability of Blockchain technology for decentralised applications in multiple domains. This paper investigates the application of Blockchain technology to address some of the key challenges in the domain of Social Business (SB). SB is a business model for investments in social causes for the socio-economic development of under-privileged communities. In this paper, we have identified and discuss principles and applications of Blockchain that enhance trust, transparency and auditability in SB activities. We outline the challenges related to creating a native cryptocurrency for SB, and barriers to infrastructure and technology adoption by different SB stakeholders.