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.

Interview James G Kobielus IBM Big Data

Here is an interview with  James G Kobielus, who is the Senior Program Director, Product Marketing, Big Data Analytics Solutions at IBM. Special thanks to Payal Patel Cudia of IBM’s communication team,for helping with the logistics for this.

Ajay -What are the specific parts of the IBM Platform that deal with the three layers of Big Data -variety, velocity and volume

James-Well first of all, let’s talk about the IBM Information Management portfolio. Our big data platform addresses the three layers of big data to varying degrees either together in a product , or two out of the three or even one of the three aspects. We don’t have separate products for the variety, velocity and volume separately.

Let us define these three layers-Volume refers to the hundreds of terabytes and petabytes of stored data inside organizations today. Velocity refers to the whole continuum from batch to real time continuous and streaming data.

Variety refers to multi-structure data from structured to unstructured files, managed and stored in a common platform analyzed through common tooling.

For Volume-IBM has a highly scalable Big Data platform. This includes Netezza and Infosphere groups of products, and Watson-like technologies that can support petabytes volume of data for analytics. But really the support of volume ranges across IBM’s Information Management portfolio both on the database side and the advanced analytics side.

For real time Velocity, we have real time data acquisition. We have a product called IBM Infosphere, part of our Big Data platform, that is specifically built for streaming real time data acquisition and delivery through complex event processing. We have a very rich range of offerings that help clients build a Hadoop environment that can scale.

Our Hadoop platform is the most real time capable of all in the industry. We are differentiated by our sheer breadth, sophistication and functional depth and tooling integrated in our Hadoop platform. We are differentiated by our streaming offering integrated into the Hadoop platform. We also offer a great range of modeling and analysis tools, pretty much more than any other offering in the Big Data space.

Attached- Jim’s slides from Hadoop World

Ajay- Any plans for Mahout for Hadoop

Jim- I cant speak about product plans. We have plans but I cant tell you anything more. We do have a feature in Big Insights called System ML, a library for machine learning.

Ajay- How integral are acquisitions for IBM in the Big Data space (Netezza,Cognos,SPSS etc). Is it true that everything that you have in Big Data is acquired or is the famous IBM R and D contributing here . (see a partial list of IBM acquisitions at at http://www.ibm.com/investor/strategy/acquisitions.wss )

Jim- We have developed a lot on our own. We have the deepest R and D of anybody in the industry in all things Big Data.

For example – Watson has Big Insights Hadoop at its core. Apache Hadoop is the heart and soul of Big Data (see http://www-01.ibm.com/software/data/infosphere/hadoop/ ). A great deal that makes Big Insights so differentiated is that not everything that has been built has been built by the Hadoop community.

We have built additions out of the necessity for security, modeling, monitoring, and governance capabilities into BigInsights to make it truly enterprise ready. That is one example of where we have leveraged open source and we have built our own tools and technologies and layered them on top of the open source code.

Yes of course we have done many strategic acquisitions over the last several years related to Big Data Management and we continue to do so. This quarter we have done 3 acquisitions with strong relevance to Big Data. One of them is Vivisimo (http://www-03.ibm.com/press/us/en/pressrelease/37491.wss ).

Vivisimo provides federated Big Data discovery, search and profiling capabilities to help you figure out what data is out there,what is relevance of that data to your data science project- to help you answer the question which data should you bring in your Hadoop Cluster.

 We also did Varicent , which is more performance management and we did TeaLeaf , which is a customer experience solution provider where customer experience management and optimization is one of the hot killer apps for Hadoop in the cloud. We have done great many acquisitions that have a clear relevance to Big Data.

Netezza already had a massively parallel analytics database product with an embedded library of models called Netezza Analytics, and in-database capabilties to massively parallelize Map Reduce and other analytics management functions inside the database. In many ways, Netezza provided capabilities similar to that IBM had provided for many years under the Smart Analytics Platform (http://www-01.ibm.com/software/data/infosphere/what-is-advanced-analytics/ ) .

There is a differential between Netezza and ISAS.

ISAS was built predominantly in-house over several years . If you go back a decade ago IBM acquired Ascential Software , a product portfolio that was the heart and soul of IBM InfoSphere Information Manager that is core to our big Data platform. In addition to Netezza, IBM bought SPSS two years back. We already had data mining tools and predictive modeling in the InfoSphere portfolio, but we realized we needed to have the best of breed, SPSS provided that and so IBM acquired them.

 Cognos– We had some BI reporting capabilities in the InfoSphere portfolio that we had built ourselves and also acquired for various degrees from prior acquisitions. But clearly Cognos was one of the best BI vendors , and we were lacking such a rich tool set in our product in visualization and cubing and so for that reason we acquired Cognos.

There is also Unica – which is a marketing campaign optimization which in many ways is a killer app for Hadoop. Projects like that are driving many enterprises.

Ajay- How would you rank order these acquisitions in terms of strategic importance rather than data of acquisition or price paid.

Jim-Think of Big Data as an ecosystem that has components that are fitted to particular functions for data analytics and data management. Is the database the core, or the modeling tool the core, or the governance tools the core, or is the hardware platform the core. Everything is critically important. We would love to hear from you what you think have been most important. Each acquisition has helped play a critical role to build the deepest and broadest solution offering in Big Data. We offer the hardware, software, professional services, the hosting service. I don’t think there is any validity to a rank order system.

Ajay-What are the initiatives regarding open source that Big Data group have done or are planning?

Jim- What we are doing now- We are very much involved with the Apache Hadoop community. We continue to evolve the open source code that everyone leverages.. We have built BigInsights on Apache Hadoop. We have the closest, most up to date in terms of version number to Apache Hadoop ( Hbase,HDFS, Pig etc) of all commercial distributions with our BigInsights 1.4 .

We have an R library integrated with BigInsights . We have a R library integrated with Netezza Analytics. There is support for R Models within the SPSS portfolio. We already have a fair amount of support for R across the portfolio.

Ajay- What are some of the concerns (privacy,security,regulation) that you think can dampen the promise of Big Data.

Jim- There are no showstoppers, there is really a strong momentum. Some of the concerns within the Hadoop space are immaturity of the technology, the immaturity of some of the commercial offerings out there that implement Hadoop, the lack of standardization for formal sense for Hadoop.

There is no Open Standards Body that declares, ratifies the latest version of Mahout, Map Reduce, HDFS etc. There is no industry consensus reference framework for layering these different sub projects. There are no open APIs. There are no certifications or interoperability standards or organizations to certify different vendors interoperability around a common API or framework.

The lack of standardization is troubling in this whole market. That creates risks for users because users are adopting multiple Hadoop products. There are lots of Hadoop deployments in the corporate world built around Apache Hadoop (purely open source). There may be no assurance that these multiple platforms will interoperate seamlessly. That’s a huge issue in terms of just magnifying the risk. And it increases the need for the end user to develop their own custom integrated code if they want to move data between platforms, or move map-reduce jobs between multiple distributions.

Also governance is a consideration. Right now Hadoop is used for high volume ETL on multi structured and unstructured data sources, or Hadoop is used for exploratory sand boxes for data scientists. These are important applications that are a majority of the Hadoop deployments . Some Hadoop deployments are stand alone unstructured data marts for specific applications like sentiment analysis like.

Hadoop is not yet ready for data warehousing. We don’t see a lot of Hadoop being used as an alternative to data warehouses for managing the single version of truth of system or record data. That day will come but there needs to be out there in the marketplace a broader range of data governance mechanisms , master data management, data profiling products that are mature that enterprises can use to make sure their data inside their Hadoop clusters is clean and is the single version of truth. That day has not arrived yet.

One of the great things about IBM’s acquisition of Vivisimo is that a piece of that overall governance picture is discovery and profiling for unstructured data , and that is done very well by Vivisimo for several years.

What we will see is vendors such as IBM will continue to evolve security features inside of our Hadoop platform. We will beef up our data governance capabilities for this new world of Hadoop as the core of Big Data, and we will continue to build up our ability to integrate multiple databases in our Hadoop platform so that customers can use data from a bit of Hadoop,some data from a bit of traditional relational data warehouse, maybe some noSQL technology for different roles within a very complex Big Data environment.

That latter hybrid deployment model is becoming standard across many enterprises for Big Data. A cause for concern is when your Big Data deployment has a bit of Hadoop, bit of noSQL, bit of EDW, bit of in-memory , there are no open standards or frameworks for putting it all together for a unified framework not just for interoperability but also for deployment.

There needs to be a virtualization or abstraction layer for unified access to all these different Big Data platforms by the users/developers writing the queries, by administrators so they can manage data and resources and jobs across all these disparate platforms in a seamless unified way with visual tooling. That grand scenario, the virtualization layer is not there yet in any standard way across the big data market. It will evolve, it may take 5-10 years to evolve but it will evolve.

So, that’s the concern that can dampen some of the enthusiasm for Big Data Analytics.

About-

You can read more about Jim at http://www.linkedin.com/pub/james-kobielus/6/ab2/8b0 or

follow him on Twitter at http://twitter.com/jameskobielus

You can read more about IBM Big Data at http://www-01.ibm.com/software/data/bigdata/

Talking on Big Data Analytics

I am going  being sponsored to a Government of India sponsored talk on Big Data Analytics at Bangalore on Friday the 13 th of July. If you are in Bangalore, India you may drop in for a dekko. Schedule and Abstracts (i am on page 7 out 9) .

Your tax payer money is hard at work- (hassi majak only if you are a desi. hassi to fassi.)

13 July 2012 (9.30 – 11.00 & 11.30 – 1.00)
Big Data Big Analytics
The talk will showcase using open source technologies in statistical computing for big data, namely the R programming language and its use cases in big data analysis. It will review case studies using the Amazon Cloud, custom packages in R for Big Data, tools like Revolution Analytics RevoScaleR package, as well as the newly launched SAP Hana used with R. We will also review Oracle R Enterprise. In addition we will show some case studies using BigML.com (using Clojure) , and approaches using PiCloud. In addition it will showcase some of Google APIs for Big Data Analysis.

Lastly we will talk on social media analysis ,national security use cases (i.e. cyber war) and privacy hazards of big data analytics.

Schedule

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Abstracts

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