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Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
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Matthew Hallett
Antoine Amend Antoine Amend
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Antoine Amend
Andrew Morgan Andrew Morgan
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Andrew Morgan
Albert Bifet Albert Bifet
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Albert Bifet
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Toc

Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem FREE CHAPTER 2. Data Acquisition 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

Access

We have thus far concentrated only on the specific ideas of ensuring that a user is who they say they are and that only the correct users can view and use data. However, once we have taken the appropriate steps and confirmed these details, we still need to ensure that this data is secure when the user is actually using it; there are a number of areas to consider:

  • Is the user allowed to see all of the information in the data? Perhaps they are to be limited to certain rows, or even certain parts of certain rows.
  • Is the data secure when the user runs analytics across it? We need to ensure that the data isn't transmitted as plain text and therefore open to man-in-the-middle attacks.
  • Is the data secure once the user has completed their task? There's no point in ensuring that the data is super secure at all stages, only to write plain text results to an insecure area.
  • Can conclusions be made from the aggregation of data? Even if the user only has access to certain rows of a dataset...
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