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Geeks for Privacy: Play Color Cipher and Visual Cryptography

Maybe the guys in Anonymous or Wikileaks can now use visual cryptography while using Snapchat to fool the NSA or CIA

Personally I think a browser with inbuilt backdoors to Tor Relays and data transfer by Bit Torrrents could be worthy a project too.

Quit the bullshit, Google- you are as evil as The Russian Communist Empire

I was just reading up on my weekly to-read list and came across this interesting method. It is called Play Color Cipher-

Each Character ( Capital, Small letters, Numbers (0-9), Symbols on the keyboard ) in the plain text is substituted with a color block from the available 18 Decillions of colors in the world [11][12][13] and at the receiving end the cipher text block (in color) is decrypted in to plain text block. It overcomes the problems like “Meet in the middle attack, Birthday attack and Brute force attacks [1]”.
It also reduces the size of the plain text when it is encrypted in to cipher text by 4 times, with out any loss of content. Cipher text occupies very less buffer space; hence transmitting through channel is very fast. With this the transportation cost through channel comes down.

ColorCipherBlocks

Reference-

http://www.ijcaonline.org/journal/number28/pxc387832.pdf

Visual Cryptography is indeed an interesting topic-

Visual cryptography, an emerging cryptography technology, uses the characteristics of human vision to decrypt encrypted
images. It needs neither cryptography knowledge nor complex computation. For security concerns, it also ensures that hackers
cannot perceive any clues about a secret image from individual cover images. Since Naor and Shamir proposed the basic
model of visual cryptography, researchers have published many related studies.

Visual_crypto_animation_demo

Visual cryptography (VC) schemes hide the secret image into two or more images which are called
shares. The secret image can be recovered simply by stacking the shares together without any complex
computation involved. The shares are very safe because separately they reveal nothing about the secret image.

Visual Cryptography provides one of the secure ways to transfer images on the Internet. The advantage
of visual cryptography is that it exploits human eyes to decrypt secret images .

ESPECIALLY SEE |THIS AND THIS

http://cacr.uwaterloo.ca/~dstinson/VCS-flag.html

and

http://cacr.uwaterloo.ca/~dstinson/VCS-pi.html

Even more fun—– visual cryptography using a series of bar codes – leaving the man in middle guessing how many sub images are there and which if at all is the real message

 

vispixel

References-

Color Visual Cryptography Scheme Using Meaningful Shares

http://csis.bits-pilani.ac.in/faculty/murali/netsec-10/seminar/refs/muralikrishna4.pdf

Visual cryptography for color images

http://csis.bits-pilani.ac.in/faculty/murali/netsec-10/seminar/refs/muralikrishna3.pdf

Other Resources

  1. http://users.telenet.be/d.rijmenants/en/visualcrypto.htm
  2. Visual Crypto – One-time Image Create two secure images from one by Robert Hansen
  3. Visual Crypto Java Applet at the University of Regensburg
  4. Visual Cryptography Kit Software to create image layers
  5. On-line Visual Crypto Applet by Leemon Baird
  6. Extended Visual Cryptography (pdf) by Mizuho Nakajima and Yasushi Yamaguchi
  7. Visual Cryptography Paper by Moni Noar and Adi Shamir
  8. Visual Crypto Talk (pdf) by Frederik Vercauteren ESAT Leuven
  9. http://cacr.uwaterloo.ca/~dstinson/visual.html
  10. t the University of Salerno web page on visual cryptogrpahy.
  11. Visual Crypto Page by Doug Stinson
  12. Simple implementation of the visual cryptography scheme based on Moni Naor and Adi Shamir, Visual Cryptography, EUROCRYPT 1994, pp1–12. This technique allows visual information like pictures to be encrypted so that decryption can be done visually.The code outputs two files. Try printing them on two separate transparencies and putting them one on top of the other to see the hidden message. http://algorito.com/algorithm/visual-cryptography

Visual Cryptography 

Ajay- I think a combination of sharing and color ciphers would prove more helpful to secure Internet Communication than existing algorithms. It also levels the playing field from computationally rich players to creative coders.

New RCommander with ggplot #rstats

 

My favorite GUI (or one of them) R Commander has a relatively new plugin called KMGGplot2. Until now Deducer was the only GUI with ggplot features , but the much lighter and more popular R Commander has been a long champion in people wanting to pick up R quickly.

 

http://cran.r-project.org/web/packages/RcmdrPlugin.KMggplot2/

RcmdrPlugin.KMggplot2: Rcmdr Plug-In for Kaplan-Meier Plot and Other Plots by Using the ggplot2 Package

 

As you can see by the screenshot- it makes ggplot even easier for people (like R  newbies and experienced folks alike)

 

This package is an R Commander plug-in for Kaplan-Meier plot and other plots by using the ggplot2 package.

Version: 0.1-0
Depends: R (≥ 2.15.0), stats, methods, grid, Rcmdr (≥ 1.8-4), ggplot2 (≥ 0.9.1)
Imports: tcltk2 (≥ 1.2-3), RColorBrewer (≥ 1.0-5), scales (≥ 0.2.1), survival (≥ 2.36-14)
Published: 2012-05-18
Author: Triad sou. and Kengo NAGASHIMA
Maintainer: Triad sou. <triadsou at gmail.com>
License: GPL-2
CRAN checks: RcmdrPlugin.KMggplot2 results

 

----------------------------------------------------------------
NEWS file for the RcmdrPlugin.KMggplot2 package
----------------------------------------------------------------

----------------------------------------------------------------

Changes in version 0.1-0 (2012-05-18)

 o Restructuring implementation approach for efficient
   maintenance.
 o Added options() for storing package specific options (e.g.,
   font size, font family, ...).
 o Added a theme: theme_simple().
 o Added a theme element: theme_rect2().
 o Added a list box for facet_xx() functions in some menus
   (Thanks to Professor Murtaza Haider).
 o Kaplan-Meier plot: added confidence intervals.
 o Box plot: added violin plots.
 o Bar chart for discrete variables: deleted dynamite plots.
 o Bar chart for discrete variables: added stacked bar charts.
 o Scatter plot matrix: added univariate plots at diagonal
   positions (ggplot2::plotmatrix).
 o Deleted the dummy data for histograms, which is large in
   size.

----------------------------------------------------------------

Changes in version 0.0-4 (2011-07-28)

 o Fixed "scale_y_continuous(formatter = "percent")" to
   "scale_y_continuous(labels = percent)" for ggplot2
   (>= 0.9.0).
 o Fixed "legend = FALSE" to "show_guide = FALSE" for
   ggplot2 (>= 0.9.0).
 o Fixed the DESCRIPTION file for ggplot2 (>= 0.9.0) dependency.

----------------------------------------------------------------

Changes in version 0.0-3 (2011-07-28; FIRST RELEASE VERSION)

 o Kaplan-Meier plot: Show no. at risk table on outside.
 o Histogram: Color coding.
 o Histogram: Density estimation.
 o Q-Q plot: Create plots based on a maximum likelihood estimate
   for the parameters of the selected theoretical distribution.
 o Q-Q plot: Create plots based on a user-specified theoretical
   distribution.
 o Box plot / Errorbar plot: Box plot.
 o Box plot / Errorbar plot: Mean plus/minus S.D.
 o Box plot / Errorbar plot: Mean plus/minus S.D. (Bar plot).
 o Box plot / Errorbar plot: 95 percent Confidence interval
   (t distribution).
 o Box plot / Errorbar plot: 95 percent Confidence interval
   (bootstrap).
 o Scatter plot: Fitting a linear regression.
 o Scatter plot: Smoothing with LOESS for small datasets or GAM
   with a cubic regression basis for large data.
 o Scatter plot matrix: Fitting a linear regression.
 o Scatter plot matrix: Smoothing with LOESS for small datasets
   or GAM with a cubic regression basis for large data.
 o Line chart: Normal line chart.
 o Line chart: Line char with a step function.
 o Line chart: Area plot.
 o Pie chart: Pie chart.
 o Bar chart for discrete variables: Bar chart for discrete
   variables.
 o Contour plot: Color coding.
 o Contour plot: Heat map.
 o Distribution plot: Normal distribution.
 o Distribution plot: t distribution.
 o Distribution plot: Chi-square distribution.
 o Distribution plot: F distribution.
 o Distribution plot: Exponential distribution.
 o Distribution plot: Uniform distribution.
 o Distribution plot: Beta distribution.
 o Distribution plot: Cauchy distribution.
 o Distribution plot: Logistic distribution.
 o Distribution plot: Log-normal distribution.
 o Distribution plot: Gamma distribution.
 o Distribution plot: Weibull distribution.
 o Distribution plot: Binomial distribution.
 o Distribution plot: Poisson distribution.
 o Distribution plot: Geometric distribution.
 o Distribution plot: Hypergeometric distribution.
 o Distribution plot: Negative binomial distribution.

Color Palettes in R using RColorBrewer #rstats

The lovely colors at http://ColorBrewer.org can be used for much better color palettes in R.

library(RColorBrewer)

display.brewer.all()

and we use the function

brewer.pal(N,”Name”) as the col  parameter for the new color palettes

where we can see name of palettes  from the list above

 

data(VADeaths)
par(mfrow=c(2,3))
 hist(VADeaths,col=brewer.pal(3,"Set3"),main="Set3 3 colors")
 hist(VADeaths,col=brewer.pal(3,"Set2"),main="Set2 3 colors")
 hist(VADeaths,col=brewer.pal(3,"Set1"),main="Set1 3 colors")
 hist(VADeaths,col=brewer.pal(8,"Set3"),main="Set3 8 colors")
 hist(VADeaths,col=brewer.pal(8,"Greys"),main="Greys 8 colors")
 hist(VADeaths,col=brewer.pal(8,"Greens"),main="Greens 8 colors")
Created by Pretty R at inside-R.org

Colors from [http://www.ColorBrewer.org] by Cynthia A. Brewer, Geography, Pennsylvania State University
• Erich Neuwirth (2011). RColorBrewer: ColorBrewer palettes. R package version 1.0-5. [http://CRAN.R-project.org/package=RColorBrewer]
Note-ColorBrewer is Copyright (c) 2002 Cynthia Brewer, Mark Harrower, and The Pennsylvania State University. All rights reserved. The ColorBrewer palettes have been included in the R package with permission of the copyright holder.

Sunburst and Cartograms in R

There are still some graphs that cannot be yet made in R using a straightforward function or package.

One is sunburst (which is  radial kind of treemap-that can be made in R). See diagrams below to see the difference. Note sunburst is visually similar to coxcomb (Nightangle) graphs. Coxcombs can also be manipulated and made- but I am yet to find a straight package to make coxcomb using a single function _histdata package in R comes close in terms on historical datasets.

The Treemap uses a rectangular, space-filling slice-and-dice technique to visualize objects in the different levels of a hierarchy. The area and color of each item corresponds to an attribute of the item as well.

The Sunburst technique is an alternative, space-filling visualization that uses a radial rather than a rectangular layout. An example Sunburst display is shown below. citation- http://www.cc.gatech.edu/gvu/ii/sunburst/

Coxcomb Below-

 

 

Other is cartogram -whose packages are MIA  -RCartogram is very basic package http://www.omegahat.org/Rcartogram/ - It is better to use Toad Scraper software than R for this kind of map.

Cartograms are  used to produce spatial plots where the boundaries of regions can be transformed to be proportional to density/counts/populations. This is illustrated in plots such as

Mark Newman’s plot of People living with HIV/AIDS
Citation: Friendly, Michael (2001), Gallery of Data Visualization, Electronic document, http://www.datavis.ca/gallery/,Accessed: 03/23/2012 18:23:33

Analytics 2011 Conference

From http://www.sas.com/events/analytics/us/

The Analytics 2011 Conference Series combines the power of SAS’s M2010 Data Mining Conference and F2010 Business Forecasting Conference into one conference covering the latest trends and techniques in the field of analytics. Analytics 2011 Conference Series brings the brightest minds in the field of analytics together with hundreds of analytics practitioners. Join us as these leading conferences change names and locations. At Analytics 2011, you’ll learn through a series of case studies, technical presentations and hands-on training. If you are in the field of analytics, this is one conference you can’t afford to miss.

Conference Details

October 24-25, 2011
Grande Lakes Resort
Orlando, FL

Analytics 2011 topic areas include:

  • Data Mining
  • Forecasting
  • Text Analytics
  • Fraud Detection
  • Data Visualization (more…)

Analyzing Conversations on Twitter

If you are a marketing , analyst relationship, public relationship or a product manager who uses or abuses social media, you sometimes need to track what influencers and analysts are saying. A tool called Bettween allows you to capture public conversations between two influential (or interesting) tweeps.

See conversations between Neil Raden http://www.beyeblogs.com/raden/ and Curt Monash http://www.dbms2.com/ two noted BI gurus

http://bettween.com/neilraden/curtmonash

  • @NEILRADEN66
  • @CURTMONASH61
  • TOTAL MESSAGES127
  • SHARE CONVERSATION


unless Google decides to license its Wave technology to Twitter for separate encrypted , or public tweets. :) They do share some history and employees (cough cough) or Twitter waits to create or better its public /protected tweet mode to be more granular

http://bettween.com/neilraden/curtmonash#statistics

tools to analyze Twitter conversations in SAS

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