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Privacy in Decentralized Cryptocurrencies

Communications of the ACM

Cryptocurrencies promise to revolutionize the financial industry, forever changing the way we transfer money. Instead of relying on a central authority (for example, a government entity or a bank) to issue and manage money, cryptocurrencies rely on the mathematical design and security proofs of the underlying cryptographic protocols. Using cryptography and distributed algorithms, cryptocurrencies offer a fully decentralized setting where no single entity can monitor or block the transfer of funds. Cryptocurrencies have grown from early prototypes to a global phenomenon with millions of participating individuals and institutions.17 Bitcoin28 was the first such currency launched in 2009 and in the years since has grown to a market capitalization of over $15 billion (as of January 2017). This has led to the emergence of many alternative cryptocurrencies with additional services or different properties as well as to a fruitful line of academic research. Apart from its other benefits (decentralized architecture, small transaction fees, among others), Bitcoin's design attempts to provide some level of "pseudonymity" by not directly publishing the identities of the participating parities. In practice, there is no bound on the number of addresses a user can create; therefore there exists no single address a user can be related with. However, this pseudonymity is far from the desired unlinkability property in centralized e-cash protocols,11 where when Alice sends an amount to Bob, the original source of these funds cannot be deduced. The reason for this problem is that in most decentralized cryptocurrencies all transaction information (payer and payee address, amount, among others) is publicly visible, stored in a distributed data structure called blockchain (for example, see www.blockchain.info). Therefore, an attacker can easily observe how money flows. In this article, we review widely studied mechanisms for achieving privacy in blockchain-based cryptocurrencies such as Bitcoin. We focus on mixing services that can be used as a privacy overlay on top of a cryptocurrency; and privacy-preserving alternative coins that, by design, aim to achieve strong privacy properties. We discuss and compare the privacy guarantees achieved by known mechanisms, as well as their performance and practical adoption.


Building Intelligent Systems 25th April 2018 For Librarians

@machinelearnbot

Machine Learning Scientist and Apress author of Building Intelligent Systems: A Guide to Machine Learning Engineering, Geoff Hulten, gives an overview of what you need to know when approaching your own applied machine learning project. Intelligent Systems connect machine learning with users to create positive impact for your organization and customers. This webinar introduces an approach to building intelligent systems that has been proven in some of the largest, most important software systems in the world. Geoff covers the five key elements that must be balanced to make your Intelligent System effective and to run it efficiently over its life cycle.


Emerging Trends in Big Data, Analytics, Machine Learning, and Interne…

#artificialintelligence

Customer requirements are evolving Data variety and data volumes are increasing rapidly Customers want to democratize access to data in a governed way Security and cost remain key decision factors Analytic needs are evolving beyond batch reports to real-time and predictive Customers are looking to incorporate voice, image recognition, and IoT use cases into applications 3. 2018, Amazon Web Services, Inc. or its Affiliates. Traditionally, analytics used to look like this 4. 2018, Amazon Web Services, Inc. or its Affiliates. Exchange Data • 12 equities markets • 4 options markets SIP Data • SIP trades • SIP NBBO • OPRA Broker Dealer data • 4000 plus firms Third Party Data • Bloomberg • Thompson Reuters • DTCC • OCC Management Amazon S3 Amazon Glacier Intake Linkage Normalization Validation Analytics Amazon Redshift Amazon EMR Machine Learning API API RDS IAM KMS Usage Stats • 33k • 20Pb Structured and unstructured data Millions of documents 25K data checks daily Normalization 33,000 servers daily Centralized data Normalized data Integrated data Discoverable Direct data query ML/AI platforms Applications/ Visualizations 19. Serverless Analytics Deliver cost-effective analytic solutions faster $ 24. Video Amazon Rekognition Video 1. Video is uploaded and stored to S3 2. Rekognition Video creates metadata for people and objects with time segments for search 4. Lambda also pushes the metadata and confidence scores into Elasticsearch 3. The output is persisted as metadata into DynamoDB to ensure durability Amazon Rekognition Video AWS LambdaAmazon S3 Amazon Elasticsearch Amazon DynamoDB 26.


Takeaways from the Google Speech Summit 2018

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Generative Text-to-Speech Synthesis, Heiga Zen, Research Scientist Abstract: Recent progress in deep generative models and its application to text-to-speech (TTS) synthesis has made a breakthrough in the naturalness of artificially generated speech.


The Road Ahead for Autonomous Cars and Auto Insurance

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The death of a pedestrian who was struck by an autonomous vehicle in Tempe, Arizona, has brought fresh scrutiny to the accelerating development of self-driving cars. The accident on March 18 is bound to be studied exhaustively, both to determine fault and to assess and refine the overall safety of autonomous systems. According to accounts of the accident, the vehicle, outfitted to test Uber's autonomous driving system, struck a woman at night as she pushed her bicycle across a road outside of a designated crosswalk. Video of the crash, released by Tempe police, shows a woman emerging from a darkened area seconds before she was struck; in the same span of time, the safety driver looks down multiple times for reasons that aren't clear. Uber pledged its full cooperation in the unfolding investigation but has already reached a settlement with some of the victim's family members, while others have come forward, according to multiple news reports.


Chatbots as your Doctors - Maruti Techlabs

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Over the last 10 years, we have come to see robots perform and execute jobs that were once exclusive to humans – be it, manufacturing cars or filling warehouse orders. As of today, we are no strangers to the fact that there are multiple industries that AI/ML have significantly impacted over the last couple years. However, the integration of Artificial Intelligence in healthcare with a chatbot as your doctor is set to witness a significant paradigm shift. We are already seeing image recognition algorithms assist in detecting diseases at an astounding rate and are only beginning to scratch the surface. Chatbots are slowly being adopted within healthcare, albeit being in their nascent stage.


The Difference Between Artificial Intelligence and Machine Learning

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Confused whether artificial intelligence and machine learning are the same thing? Anna Brown asks the experts to explain. LEARN MORE ABOUT ARTIFICIAL INTELLIGENCE https://www.sas.com/en_us/insights/an... SAS DOES AI - CHECK OUT SAS AI SOLUTIONS https://www.sas.com/en_us/solutions/a... LEARN MORE ABOUT MACHINE LEARNING https://www.sas.com/en_us/insights/an... WEBINAR: INTRODUCTION TO MACHINE LEARNING In this this webinar, Wayne Thompson of SAS delves into those issues and provides an overview of machine learning, as well as key business applications of this technique, including fraud detection, model factories and recommendation systems. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW .



AI in the enterprise: A framework for success - TotalCIO

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Developing an AI use case that lays out what the project will cost, the value it will provide and the potential risks it will bring can be a head scratcher for CIOs. AI in the enterprise is uncharted territory for many companies. What exactly is digital transformation? You may hear the term often, but everyone seems to have a different definition. See how our experts define digitization, and how you can get started in this free guide.


AI in the enterprise: A framework for success - TotalCIO

#artificialintelligence

Developing an AI use case that lays out what the project will cost, the value it will provide and the potential risks it will bring can be a head scratcher for CIOs. AI in the enterprise is uncharted territory for many companies. This complimentary document comprehensively details the elements of a strategic IT plan that are common across the board – from identifying technology gaps and risks to allocating IT resources and capabilities. You forgot to provide an Email Address. This email address doesn't appear to be valid.