Bitcoin has created a new exchange paradigm within which financial transactions can be trusted without an intermediary. This premise of a free decentralized transactional network however requires, in its current implementation, unrestricted access to the ledger for peer-based transaction verification. A number of studies have shown that, in this pseudonymous context, identities can be leaked based on transaction features or off-network information. In this work, we analyze the information revealed by the pattern of transactions in the neighborhood of a given entity transaction. By definition, these features which pertain to an extended network are not directly controllable by the entity, but might enable leakage of information about transacting entities. We define a number of new features relevant to entity characterization on the Bitcoin Blockchain and study their efficacy in practice. We show that even a weak attacker with shallow data mining knowledge is able to leverage these features to characterize the entity properties.

Characterizing Entities in the Bitcoin Blockchain.

The question "Can big data and HPC infrastructure converge?" has important implications for many operators and clients of modern computing. However, answering it is challenging. The hardware is currently different, and fast evolving: big data uses machines with modest numbers of fat cores per socket, large caches, and much memory, whereas HPC uses machines with larger numbers of (thinner) cores, non-trivial NUMA architectures, and fast interconnects. In this work, we investigate the convergence of big data and HPC infrastructure for one of the most challenging application domains, the highly irregular graph processing. We contrast through a systematic, experimental study of over 300,000 core-hours the performance of a modern multicore, Intel Knights Landing (KNL) and of traditional big data hardware, in processing representative graph workloads using state-of-the-art graph analytics platforms. The experimental results indicate KNL is convergence-ready, performance-wise, but only after extensive and expert-level tuning of software and hardware parameters.

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Exploring HPC and Big Data Convergence: A Graph Processing Study on Intel Knights Landing

Motivated by the need to extract knowledge and value from interconnected data, graph analytics on big data is a very active area of research in both industry and academia To support graph analytics efficiently a large number of in memory graph libraries, graph processing systems and graph databases have emerged Projects in each of these categories focus on particular aspects such as static versus dynamic graphs, off line versus on line processing, small versus large graphs, etc While there has been much advance in graph processing in the past decades, there is still a need for a fast graph processing, using a cluster of machines with distributed storage In this paper, we discuss a novel distributed graph database called System G designed for efficient graph data storage and processing on modern computing architectures In particular we describe a single node graph database and a runtime and communication layer that allows us to compose a distributed graph database from multiple single node instances From various industry requirements, we find that fast insertions and large volume concurrent queries are critical parts of the graph databases and we optimize our database for such features We experimentally show the efficiency of System G for storing data and processing graph queries on state-of-the-art platforms

Song Zhang

Papers

System G Distributed Graph Database.