BlockTag: Design and applications of a tagging system for blockchain analysis

Annotating blockchains with auxiliary data is useful for many applications. For example, e-crime investigations of illegal Tor hidden services, such as Silk Road, often involve linking Bitcoin addresses, from which money is sent or received, to user accounts and related online activities. We present BlockTag, an open-source tagging system for blockchains that facilitates such tasks. We describe BlockTag's design and present three analyses that illustrate its capabilities in the context of privacy research and law enforcement

System and Method for Truth Discovery in social media Big Data

Within the span of enormous information and the coming of numerous advancements in the communication technologies, at every tick of the clock, enormous sums of information is produced from different sources. One such source of data generation is social media. However, such data carries much of the noisy, uncertain, and untrustworthy data. In this way, finding independable information from loud information is one of the characteristic challenges of huge information focusing on the esteem characteristic of enormous information. Therefore, in this article, an attempt is made to target a few challenges arriving from “misinformation spread”, “data sparsity” or the “long-tail wonder” in the domain of social media data analytics. The study uses an instance from the Online Social Network (OSN) datasets to develop scalable to wide-range social sensing by consolidating Scalable Robust Trust Discovery (SRTD) plots to address the mentioned challenges utilizing the distributed parallel computing framework. The dataset picked for investigation includes 128,483 tweets which incorporates 20% deception, 80% retweets bringing about 0.05 milliseconds utilizing Spark parallel processing

MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data

Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. For example, symptom \emph{runny nose} highly indicates the existence of disease \emph{whooping cough} when the patient is a baby rather than the people at other ages. Such conditions for medical knowledge are crucial for decision-making in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the amount of available EMRs is limited due to reasons such as regularization. Meanwhile, a large amount of medical question answering (QA) data is available, which can greatly help the studied task. However, the quality of medical QA data is quite diverse, which may degrade the quality of the discovered medical knowledge conditions. In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation. We conduct series of experiments on real-world medical datasets to demonstrate that the proposed method can discover meaningful and accurate conditions for medical knowledge by leveraging both EMR and QA data. Further, the proposed method is tested on synthetic datasets to validate its effectiveness under various scenarios.Comment: Accepted as CIKM2019 long pape