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10 use-cases for privacy-preserving synthetic data
Fast-evolving data protection laws are constantly reshaping the data landscape. The organizational ability to overcome sensitive data usage restrictions while safeguarding customer privacy will be a key driver of tomorrow’s successful businesses. This blog presents ten concrete applications for privacy-preserving synthetic…Continue
Added by Elise Devaux on August 5, 2020 at 6:59am — No Comments
This blog takes a closer look at the concept of privacy-preserving synthetic data. It answers the question “what is synthetic data” and looks at the origin of synthetic data in the context of data privacy. It also presents one way of generating privacy-preserving synthetic data and its benefits for organizations.…Continue
Added by Elise Devaux on July 2, 2020 at 11:30am — No Comments
As I learn about data privacy, I’m starting to realize how large the ecosystem is. I focused here on a category that spans across the data privacy landscape, Privacy Enhancing Technologies (PETs). In the post, I cover:…
Added by Elise Devaux on June 12, 2020 at 2:14am — No Comments
Bernouilli lattice processes may be one of the simplest examples of point processes, and can be used as an introduction to learn about more complex spatial processes that rely on advanced measure theory for their definition. In this article, we show the differences and analogies between Bernouilli lattice processes on the standard rectangular or hexagonal grid, and the Poisson process, including convergence of discrete lattice processes to continuous Poisson process, mainly in two…Continue
Added by Vincent Granville on June 5, 2020 at 1:11pm — No Comments
Summary: Explaining data science to a non-data scientist isn’t as easy as it sounds. You may know a lot about math, tools, techniques, data, and computer architecture but the question is how do you explain this briefly without getting buried in the detail. You might try this approach.
Added by Vincent Granville on June 4, 2020 at 5:05pm — No Comments
Product of two large primes are at the core of many encryption algorithms, as factoring the product is very hard for numbers with a few hundred digits. The two prime factors are associated with the encryption keys (public and private keys). Here we describe a new approach to factoring a big number that is the product of two primes of roughly the same size. It is designed especially to handle this problem and identify flaws in encryption algorithms. …Continue
Added by Vincent Granville on May 27, 2020 at 12:20pm — No Comments
Added by Elise Devaux on May 23, 2020 at 1:00pm — No Comments
We discuss a simple trick to significantly accelerate the convergence of an algorithm when the error term decreases in absolute value over successive iterations, with the error term oscillating (not necessarily periodically) between positive and negative values.
We first illustrate the technique on a well known and simple case: the computation of log 2 using its well know, slow-converging series. We then discuss a very interesting and more complex case, before finally focusing on a…Continue
Added by Vincent Granville on May 5, 2020 at 5:37pm — No Comments
One of the main challenges in data science projects is managing stakeholder expectations. Often those in the business will have little idea of the complexity and timescales of seemingly simple tasks.
Consider sourcing data. In some organisations, with a non-collaborative culture, something as simple as getting a file of data from IT can take weeks. Add on time to check the data, spend time with someone to explain it, handle revisions and…Continue
Added by Andrew Watson on May 1, 2020 at 7:00am — No Comments
The methodology described here has broad applications, leading to new statistical tests, new type of ANOVA (analysis of variance), improved design of experiments, interesting fractional factorial designs, a better understanding of irrational numbers leading to cryptography, gaming and Fintech applications, and high quality random numbers generators (and when you really need them). It also features exact arithmetic / high performance computing and distributed algorithms to compute millions of…Continue
Added by Vincent Granville on February 29, 2020 at 11:00pm — No Comments
Summary: The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out the big news is how much more capable all the platforms have become. Of course there are also some interesting winner and loser stories.
The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out for 2020. The really big news is how many excellent choices are now available. In a remarkable move, the whole field…Continue
Added by Vincent Granville on February 21, 2020 at 9:25am — No Comments
In this notebook, we try to predict the positive (label 1) or negative (label 0) sentiment of the sentence. We use the UCI Sentiment Labelled Sentences Data Set.
Sentiment analysis is very useful in many areas. For example, it can be used for internet conversations moderation. Also, it is possible to predict ratings that users can assign to a certain product (food, household appliances, hotels,…Continue
Added by Vincent Granville on February 19, 2020 at 8:42pm — No Comments
Probably the worst error is thinking there is a correlation when that correlation is purely artificial. Take a data set with 100,000 variables, say with 10 observations. Compute all the (99,999 * 100,000) / 2 cross-correlations. You are almost guaranteed to find one above 0.999. This is best illustrated in may article How to Lie with P-values (also discussing…Continue
Added by Vincent Granville on February 7, 2020 at 9:48am — No Comments
Fermat's last conjecture has puzzled mathematicians for 300 years, and was eventually proved only recently. In this note, I propose a generalization, that could actually lead to a much simpler proof and a more powerful result with broader applications, including to solve numerous similar equations. As usual, my research involves a significant amount of computations and experimental math, as an exploratory step before stating new conjectures, and eventually trying to prove them. The…Continue
Added by Vincent Granville on January 30, 2020 at 1:09am — No Comments
Hundreds of programming languages dominate the data science and statistics market: Python, R, SAS and SQL are standouts. If you're looking to branch out and add a new programming language to your skill set, which one should you learn? This one picture breaks down the differences between the four languages.…Continue
Added by Vincent Granville on January 28, 2020 at 8:41pm — No Comments
While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reasonably good familiarity of such details. A solid understanding of the intuition behind such algorithms not only helps in appreciating the logic behind them but also helps in making conscious decisions about their applicability in real life cases. There are several graph based algorithms and most notable are the shortest path…Continue
Added by Vincent Granville on January 21, 2020 at 10:12am — No Comments
In 2019, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. The key differences are as follows:
Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users. TensorFlow 2.0 promotes TensorFlow Keras for model experimentation and Estimators…Continue
Added by Vincent Granville on January 9, 2020 at 9:49am — No Comments
Summary: AI/ML itself is the next big thing for many fields if you’re on the outside looking in. But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.
Added by Vincent Granville on January 7, 2020 at 7:41am — No Comments
Another good article by Ajit Joakar.
Co-relation does not equal causation – is a mantra drilled into a Data Scientist from an early age
That’s fine. But very few talk of the follow-on question ..
How exactly do you determine causation?
This problem is further compounded because most books and examples are based on standard datasets (ex: Boston, Iris etc) . These examples do not discuss…Continue
Added by Vincent Granville on December 17, 2019 at 2:30pm — No Comments
Written by Ajit Jaokar.
Firstly, there are three broad categories of algorithms:
Added by Vincent Granville on December 17, 2019 at 9:00am — No Comments