Evolutionary Computation Methods in Modern Cryptography

ABSTRACT

Meaningful attempts to produce practical solutions for the problem of an increasingly hostile environment are emerging in evolutionary computation. The problems cryptography faces are in the number and variety of ways it can be broken. One response is to build stronger defenses against increasingly powerful attacks. But what if the most powerful attacks came from the authorities who created the defense mechanisms you thought were protecting you? Powerful cryptanalysis tools and deceptive practices by authorities requires re-imagining the threat landscape. Existing solutions to this problem focus on collaboration between researchers and builders to ensure the implementation details of existing security protocols are effectively applied. Other courses of action suggest a better engagement with both software review and the creation of standards. There is no drawback to these solutions and they would complement a parallel effort to increase the diversity of encryption algorithms. Ensuring better reliability in these new algorithms means taking a look at the reliability of the building blocks of complex systems and protocols. New algorithms inspired by evolutionary computation that contribute to these building blocks is an approach that holds promise to this new threat.

(link to video presentation of this paper)

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Evolutionary Computation Methods in Modern Cryptography (video)

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Evolutionary Computation in Modern Cryptography (video) by Brad is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright, Privacy and the TPP

Sons and daughters, you ain’t getting much for free.
Chalk Circle, 1989

After 5 years of secret negotiations, and just a few short weeks before Canadians go to the poll, the Trans Pacific Partnership (TPP) trade deal has been finalized (although it still needs to be ratified in each of the member countries).

And still, no few details on the agreement have been released to Canadians.
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Genetic Algorithms in Education

In online education, there a variety of interactive group activities categorized as collaborative learning. Discussion forums is one such example of a collaborative learning strategy, group/partner project work is another. Where group dynamics play a more significant role in the learning process than other types of teaching methods, getting the right mixture of students together can have a significant effect on learning outcomes [1].
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Student performance and moral decision making in predictive technologies

Pattern recognition is a well established technique utilized by neural networks in applications that attempt to make predictions on human behaviour within a limited context. ‘Many successful researchers have used Neural Network and Decision Trees in the subject of prediction and decision making’1. In the field of education, the future behaviour of a student can be said to be important to know in order to illicit an intervention, pre-emptively by the teacher or administration. ‘Predictive’ profiling is understood to be ‘identifying who an individual is, classifying what they are and evaluating what they might be’2. In [1], the proposed modelling approach includes a feed forward network (to predict future student performance results with past data) and an additional recurrent network that maps the current state of the student’s performance to a future desired state so that an intervention, or a decision can be made. In 3, a traditional feed forward NN was used to analyze learning behaviour (represented as data collected by a web-based system), to ‘classify students according to their usual performance and finally to predict their final grades’. The utilization of the NN technique improved classification which elevated the accuracy rate of the model to reach 89.96%.
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Computational Intelligence

CI

The problem that CI is trying to solve is to how to create an intelligent system. As a  partial answer to the question ‘what is intelligence’, CI looks to nature for inspiration and tries to mimic, through algorithms, a degree of that observable intelligence 1.  If one definition of intelligence includes making a decision in the face of  uncertainty, or making inferences based on past experiences, then with some confidence we can say that process resembles ‘intelligence’. The end result is a program that can generate decisions more like a human or animal. The reason this is desirable is because there are certain problems that can be better solved by nature, like how to evolve, how to self-organize, or for a predator, how to spot the weakest in the herd. Whether or not we want computers to be better at spotting the weakest in the heard is a good question, but the difference between how computers and humans process information makes it a challenging problem to implement.  To deal with that challenge, there is a group of nature-inspired algorithms: Fuzzy logic, evolutionary computation, neural networks (also swarm optimization and genetic algorithms) — together these research fields make up what is known to be CI. 2

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Protecting privacy

Personal information is gold

Companies makes billions from it, governments and criminals go to great lengths to get it, store and analyze it. The real-world consequences for an individual who loses control of that data are identity theft, fraud, blackmail, surveillance and potential threats to personal safety and property.

The value of privacy

Most businesses require some personal information in order to function. As a contractor I have built websites for lawyers, real estate agents, restaurants, fitness companies and artists who rely on me to create information systems that meet their legal obligations for the province in which they reside.

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