Skip to main content

Hadoop use cases in the Bitcoin economy

By Desk Editor

Bitcoin as a decentralized P2P virtual currency has become a hot topic of discussion and analysis in the last few months. Due to its unregulated nature and possibility to exchange it for real life goods, services or real currency, the currency is being used for both legit and illegal trades. Further, the transaction data is publicly available online albeit with anonymous ids. In this post, lets explore the possible Bitcoin economy use cases for Hadoop ecosystem products.

Bitcoins were first suggested in 2008 by Satoshi Nakamoto. Participants of this economy acquire a Bitcoin wallet and Bitcoin addresses. The Bitcoin address send and receive Bitcoins (BTC) in a manner analogous to sending and receiving e-mails from e-mail address. The network relies on a shared transaction log called block chain where all confirmed transactions are included without any exception. The system uses a distributed consensus system called Mining that is used to confirm waiting transactions by including them in the blockchain.



Besides the known counter intuitive possibility of using Hadoop processing for mining, there are more elaborate use cases in which the products in Hadoop ecosystem can contribute positively. A few of the possible ones which can be taken up for PoC are listed below:


(1)   Determining owner entities:
Although the transaction data is available with anonymous ids only, it has been demonstrated in certain experiments to be able to club sender address to one entity and trace the transaction history of that entity. Entity here refers to an individual or group using a set of Bitcoin address. Further, combined with certain crawling data from the various websites and logs, it is possible to ascertain the real identity behind the anonymous id. For instance, in one analysis, using one of Wikileaks published Bitcoin address, it was possible to determine the other address belonging to the group. A good example of such tracing is available on blockviewer.com which utilizes Neo4j.
image source: https://github.com/thallium205/BitcoinVisualizer
Products to explore: Apache Hadoop, Apache Drill, Apache Giraph

(2)   Co-relating real life events with Bitcoin transactions and fluctuations:
Off late, there have been rumors of unrest in certain countries having linkages to Bitcoin transactions. It could be possible to analyze the Bitcoin transaction data and attribute to certain geographic entities and see co-relation with real life events.
Products to explore: Apache Hadoop, Apache Lucene, Apache Mahout
           

(3)   Securing your cluster to avoid mis-use:
Trojans have been reported to exploit the unused processing power of individual machines and network clusters to mine Bitcoins. It becomes imperative to protect your cluster against any such malware and botnets.
Products to explore: Apache Knox

(4)   Identifying illegal use sites:
There has been reported use of Bitcoins for illegal trades like drugs and narcotics. By detecting anomalous behavior in transactions along with scanning logs, it may be possible to monitor this hot trail.
Products to explore: Apache Hadoop, Apache HBase, Apache Pig, Apache Hive


While the above use cases are applicable for all currency transactions (virtual or fiat), they have been mentioned specifically for Bitcoin due to open availability of transaction data and lack of regulatory mechanism. There is a chance that the governments may crack down on this virtual currency in absence of credible monitoring tools. Here in, lies the opportunity and threat for the Bitcoin and Hadoop community to sustain this currency for legit purposes. 
Labels:

Comments

Post a Comment

Popular posts from this blog

Beyond NSA, the intelligence community has a big technology footprint

By Desk Editor
While all through the past few days the focus has been on NSA activities, the discussion has often veered around the technologies and products used by NSA. At the same time, a side discussion topic has been the larger technical ecosystem of intelligence units. CIA has been one of the more prolific users of Information Technology by its own admission. To that extent, CIA spinned off a venture capital firm In-Q-Tel in 1999 to invest in focused sector companies. Per Helen Coster of Fortune Magazine, In-Q-Tel (IQT) has been named “after the gadget-toting James Bond character Q”.
In-Q-Tel states on its website that “We design our strategic investments to accelerate product development and delivery for this ready-soon innovation, and specifically to help companies add capabilities needed by our customers in the Intelligence Community”. To that effect, it has made over 200 investments in early stage companies for propping up products. Being a not-for-profit group, unlike Private Venture capi…
Post a Comment
Read more

Data deduplication tactics with HDFS and MapReduce

By Desk Editor
As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…
Post a Comment
Read more

Top Big Data Influencers of 2015

By Sachin Ghai
2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…
1 comment
Read more