With the egoless interest of someone really passionate about his craft, Martin Hellman explained to me the limitations of the cryptographic system he helped create and how Dif e-Hellman encryption was being picked apart by modern researchers. So he’s entirely credible when he says that cryptography faces some surprising challenges.

He told me that in 1970 there was a major breakthrough in factoring, called continued fractions. The dif culty involved in factoring large numbers is what makes cryptographic systems so complex, and therefore dif cult to crack. Any advance in factoring reduces the complexity of the cryptographic system, making it more vulnerable. Then in 1980, a breakthrough pushed factoring further, thanks to Pomerance’s quadratic sieve and the work of Richard Schroeppel. “Of course, RSA [computer encryption] didn’t exist in 1970, but if it did, they would have had to double key sizes. 1980, they had to double them again. 1990 roughly, the number eld sieve roughly doubled the size of numbers again that we could factor. Notice, almost every 10 years—1970, 1980, 1990—there’s been a doubling of key size required. Except in 2000, there was no advance, no major advance since then.”

Some people, Hellman said, might look at that pattern and assume mathematicians had hit a wall. Hellman thinks differently. He invited me to think of a series of coin ips. Would I assume, he asked, that after coming up heads six times in a row, it was a certainty that the next ip would be heads?

The answer, of course, is absolutely not. “Right,” said Hellman. “We need to worry that there might be another advance in factoring.” That could weaken existing cryptographic systems or render them useless altogether.

This might not be a problem right now, but Hellman thinks we should be looking for backup systems for modern crypto in the event of future breakthroughs.

But it’s the possibility of quantum computing, and with it, quantum cryptanalysis, that could actually break every system that currently relies on encryption. Today’s computers rely on a binary 1-or-0 system to operate, with light and electricity behaving as they should. A quantum computer, on the other hand, could take advantage of quantum properties to function. It

Another advance in factoring could weaken existing cryptographic systems or render them useless altogether.



Predictive Analysis in Data Mining

Whether it is called data mining, predictive analytics, sense making, knowledge discovery, or data science, the rapid development and increased availability of advanced computational techniques have changed our world in many ways.

Good analysts are like sculptors. They can look at a data set and see the underlying form and structure. Data mining tools can function as the chisels and hammer, allowing the analysts to expose the hidden patterns and reveal the meaning in a data set so that others can enjoy its composition and beauty.

Data mining, on the other hand, is a highly intuitive, visual process that builds
on an accumulated knowledge of the subject matter, something also known as
domain expertise. While training in statistics generally is not a prerequisite for
data mining, understanding a few basic principles is important.

Throughout the data mining and modeling process, there is a fair amount of user discretion. There are some guidelines and suggestions; however, there are very few absolutes. As with data and information, some concepts in modeling are important to understand, particularly when making choices regarding accuracy, generalizability, and the nature of acceptable errors.

It really is true with predic- tive analytics and modeling that if it looks too good to be true it probably is; there is almost certainly something very wrong with the sample, the analysis, or both. Errors can come from many areas; however, the following are a few common pitfalls.

This point also highlights the importance of working with the operational personnel, the ultimate end users of most analytical products, throughout the analytical pro- cess. While they might be somewhat limited in terms of their knowledge and understanding of the particular software or algorithm, their insight and per- ception regarding the ultimate operational goals can signi cantly enhance the decision-making process when cost/bene t and error management issues need to be addressed.

Some events are so unique or rare that they are referred to as “Black Swans.” “True” Black Swans cannot be predicted or anticipated; and by extension, they cannot be prevented or thwarted. More recently, though, the concept of “Anticipatory Black Swans” has been introduced. In contrast to the True Black Swans, an Anticipatory Black Swan “can be known beforehand” as com- pared to “what truly is a surprise.”6 Like data mining generally, the proposed approach to both types of Black Swans is to, “ask the right question at the right time (and be wise enough to understand the response or appreciate the signs)…[which] could lead to success in the matter of anticipating what can be anticipated, and at least understanding sooner the impact of what cannot be anticipated.”

It is important that we clearly recognize what these tools can and cannot accomplish, though. Instead, it is all about increasing the likelihood that a desired outcome will occur – at the right time, the First time. These concepts of increased likelihood and timeliness are what make applying it to decision making so enticing.”These are very worthy, yet attainable goals for operational public safety and security analysis. With that objective in mind, I wish you well, and encourage you to go forward and do good.

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Challenges of Developing Business Information System

Have someone ever imagine how bank ATM machines provide you secure info about your bank account or a real-time interactive touch-screen flight stat machine on airport? In terms of business and engineering it’s known as information system (IS). Business Information system is a set of interrelated component (Departments or might be technical hardware) that collect (Input), manipulate it based on the knowledge (Process) and disseminate information (Output) and this information provides corrective reactions on business organization. Information is a single variable which enables the most powerful strategic decision making and in spite of raw data and business/technical knowledge it also requires accuracy, accessibility, flexible, economical, relevant, reliable, secure, simple and timely. Information system are used in almost every possible occupation and organization. Small business owners use information system to reach customer or sales representatives use information systems to advertise product and communicate with customer and analyze purchasing trends. At organizational level the main IS assist companies to cut costs and increase profit in simple business language. Technically, it simply provides easy access for every person working in organization to manage, access and create business records.
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2015 Market Analysis: Cloud Computing in Health care

After reading 2015 market reach of cloud computing in healthcare on various blogs and press release of major cloud service provider recently, my focus diverted on one of the interesting New York Time’s blog posted by quentin hardy with the title ‘The era of Cloud Computing’ where he explains the Cloud services and it’s diverse cost effective economic benefits and technological developments of virtual web of servers interlinking together. According to recent interview; Mark DePristo, a vice president for SynapDx said “Without the cloud I’d need $1 million, plus staff, just for the computer,” but with cloud computing SynapDx spends $25,000 a month instead. On the other hand, technically cloud server management and it’s integrated data centers are more cheaper in cost and comprises 80% more reliable in data redundancy and uplink in service than traditional bare metal servers computing.

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Difference in Information Privacy and System Security regulations


Legals course of action in Information Technology generally speculate information privacy and system security despite being both are typically managed and handled together. Presumably system security is a co-related of information privacy, and so does it serves information system. However, this point of view is so much distorted of the relationship between two sectors.

According to legal institutions in various countries, acknowledge information privacy and system security as separate institutional objectives to reduce the risks of over-conflicts of various data security’s objectives which are counter against information privacy. Information privacy can be defined as ‘collective advantage for group of users, with respect to common interest held by each individual user’. Simultaneously, it’s a right of individual deciding what to do with that information regarding how personal information should be used, processed and even stored on private system or either public system.

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