Battleground states prime point of digital attacks to delay election results

In a normal election cycle, battleground states are prime areas to win or lose an election. Yet as election campaigning, electoral fund raising, and even voting itself has gone digital, the ease by which people can use has not been matched by security.

This is due to systematic denial of funds by CTOs and CIOs to digital security for both campaigns as well as Federal and local digital cyber agencies. Can the USA prevent cyber attack interference in key battleground states in this election cycle.

Just as 3D printing has evolved to make guns and will evolve further, electronic manipulation of voting machines has evolved further- but security budgets and priorities have not. What about Postal ballots? Can they be tampered or intercepted with.

Who benefits when doubt is sown in the minds of voters? As Al Gore. He invented the internet.

Who benefits when a few districts in Ohio show electronic tampering?

Quo Vadis? (Where are you going?) Quis custodiet ipsos custodes? (Who guards the guardians)

Rumours on rigging would just use the algorithm (dīvide et īmpera) and if backed even by a few slivers of actual cyber attacks and tampering would undermine it even more. Yes there is no way to protect ALL the voting systems so its cyber football game of interception – and the current lack of big time offensive weapons  as rebuttal in cyber attacks makes remote attacks on election systems both possible and plausible.

there are 9,000 jurisdictions in the United States that have a hand in carrying out the balloting, many of them with different ways of collecting, tallying and reporting votes.

(sighs and goes back to watching the Olympics)

a geek rap video about predictive analytics

Left brain thinking is verbal and analytical. Right brain is non-verbal and intuitive, using pictures rather than words

Dr Eric Siegel demonstrates both here

Eric was an Assistant Professor of Computer Science at Columbia University in New York City from 1997 – 2001.

Ph.D., founder of Predictive Analytics World and Text Analytics World,  Executive Editor of the Predictive Analytics Times,  is the author of the bestselling, award-winning Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die,

Conflict of Interest Disclaimer-I write poetry here

Should Agencies like Secret Service protect digital assets of leaders or friends or family during elections or later

The revelation that Russian Intel (or Snowden working with Russian Intel) hacked into H R Climton’s Campaign Surveys should be both bad news as well as good news to cyber activists.

First of all it increases the demand in terms of jobs for legal cyber security

Secondly it pinpoints the need of security as an important component to decision makers at a time when they are most likely to pay attention ( oh ! My website got hacked! You got my attention!)

However the bad news is

Digital Assets of protected members would be secured by same agencies ( or different agencies) – a jurisdiction nightmare

Hacking into friends of friends, family after official government protection over is a crime, but when Govts hack it is difficult for a teenager /friend of teenager  who hacked their Facebook. Ditto goes for Senate Staffers or Staffers combating cyber crime.

Will cyber crime and cyber war between nations make things personal not just business, due to the ease, low cost and plausible deniability

(Note- this is a strategic what if scenario, No Pokemons were hurt during the making of this post)

Psalm 2

Why do the nations conspire[a]
    and the peoples plot in vain?
The kings of the earth rise up
    and the rulers band together

(unrelated bonus


Data Science for Olympics and lack of Reproducible Research

Despite the plethora of data generated in Sports, there is not much open data for Olympics and one wonders why if sharing best practices and data openly on what works and what does not can reduce the level of Russian athletes being banned in a cylical cold war era game.

Some links I found useful

Could data mining techniques accurately predict the medal counts at the Olympics? A predictive model could give us an estimate of the number of medals each nation might win; but how close could we get to the actual outcomes? It was a tantalizing project …

Sochi-Ru By Dan Graettinger with Tim Graettinger

• Which nation will bring home the most medals at the upcoming Winter Olympics in Sochi, Russia?

• Will any nation from Africa, South America, or the Middle East finally break through and win a medal?

• Why do some nations win a bundle of medals while others win only a few?

• Can data mining give us the answers to these questions?


the Graettinger brothers do? They used a seemingly  standard methodology: learn from the past to predict the future.  More precisely, they used past Olympics results to build a predictive model.  Each country is represented by a feature vector, i.e. a set of quantities drawn form several categories:

  • Economic
  • Population
  • Human Development
  • Geography
  • Religion
  • Politics and Freedom

Then they used a standard technique known as linear regression to find which set of features were best for predicting medal count.  I was reading their blog post with great interest until I saw what were the most meaningful features found by the linear regression algorithm:

  • Geographic area
  • GDP per capita
  • Value of Exports
  • Latitude of Nation’s Capital


I was able to find data in many categories:

  • Economic
  • Population
  • Human Development
  • Geography
  • Religion
  • Politics and Freedom

Thankfully, there were some good sources out there[f3], and I collected enough data that I felt I had a good chance to predict some meaningful outcomes.  But would it be enough?  There is more than one way to go about predicting the medal count at the Olympics, and the route before me was the “30,000 feet” approach.

So any takers?

Hackers for Hacking the Olympics🙂



Latest DecisionStats Intern

Congratulations to our latest intern for completing the intensive internship at DecisionStats . See work done by here here-

Her latest blog post tries to use Python to understand police shootings in USA


Previous Interns wrote great Python code and R code

see (Sarah Masud and Farheen)


Anshul Gupta

Cricket Analysis –



Chandan Routray

The first Intern


Some points for future interns at DecisionStats-

  1. We normally dont pay interns anything
  2. 80 % interns drop out or are let go because they cannot keep up with the assignments
  3. Remaining 20% usually learn a lot in the intensive program
  4. Internships are like a free boot camp
  5. No more internships till June 2017 because I am trying to write a book
  6. Some research assistantships might be available in December 2016 to help with some code or Lyx formatting for the former
  7. See my LinkedIn profile for reviews given by the 20% interns who manage to stick around
  8. I usually emphasize writing, polyglot tools (both R, SAS and Python) , logical thinking and concise communication for my interns
  9. I usually treat them as students since I dont work for or in a university. That might change as I try and transition out from business to academic research options for a non Phd