A model to assess organisational information privacy maturity against the Protection of Personal Information Act

Includes bibliographical references.Reports on information security breaches have risen dramatically over the past five years with 2014 accounting for some high-profile breaches including Goldman Sachs, Boeing, AT&T, EBay, AOL, American Express and Apple to name a few. One report estimates that 868,045,823 records have been breached from 4,347 data breaches made public since 2005 (Privacy Rights Clearing House, 2013). The theft of laptops, loss of unencrypted USB drives, hackers infiltrating servers, and staff deliberately accessing client’s personal information are all regularly reported (Park, 2014; Privacy Rights Clearing House, 2013) . With the rise of data breaches in the Information Age, the South African government enacted the long awaited Protection of Personal Information (PoPI) Bill at the end of 2013. While South Africa has lagged behind other countries in adopting privacy legislation (the European Union issued their Data Protection Directive in 1995), South African legislators have had the opportunity to draft a privacy Act that draws on the most effective elements from other legislation around the world. Although PoPI has been enacted, a commencement date has still to be decided upon by the Presidency. On PoPI’s commencement date organisations will have an additional year to comply with its requirements, before which they should: review the eight conditions for the lawful processing of personal information set out in Chapter three of the Act; understand the type of personal information they process ; review staff training on mobile technologies and limit access to personal information; ensure laptops and other mobile devices have passwords and are preferably encrypted; look at the physical security of the premises where personal data is store d or processed; and, assess any service providers who process in formation on their behalf. With the demands PoPI places on organisations this research aims to develop a prescriptive model providing organisations with the ability to measure their information privacy maturity based on “generally accepted information security practices and procedure s” ( Protection of Personal Information Act, No.4 of 2013 , sec. 19(3)) . Using a design science research methodology, the development process provides three distinct design cycles: 1) conceptual foundation 2) legal evaluation and 3) organisational evaluation. The end result is the development of a privacy maturity model that allows organisations to measure their current information privacy maturity against the PoPI Act. This research contributes to the knowledge of how PoPI impacts on South African organisations, and in turn, how organisations are able to evaluate their current information privacy maturity in respect of the PoPI Act. The examination and use of global best practices and standards as the foundation for the model, and the integration with the PoPI Act, provides for the development of a unique yet standards-based privacy model aiming to provide practical benefit to South African organisations

Naive Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages

Web classification has been attempted through many different technologies. In this study we concentrate on the comparison of Neural Networks (NN), Naïve Bayes (NB) and Decision Tree (DT) classifiers for the automatic analysis and classification of attribute data from training course web pages. We introduce an enhanced NB classifier and run the same data sample through the DT and NN classifiers to determine the success rate of our classifier in the training courses domain. This research shows that our enhanced NB classifier not only outperforms the traditional NB classifier, but also performs similarly as good, if not better, than some more popular, rival techniques. This paper also shows that, overall, our NB classifier is the best choice for the training courses domain, achieving an impressive F-Measure value of over 97%, despite it being trained with fewer samples than any of the classification systems we have encountered

A new extension of fuzzy sets using rough sets: R-fuzzy sets

This paper presents a new extension of fuzzy sets: R-fuzzy sets. The membership of an element of a R-fuzzy set is represented as a rough set. This new extension facilitates the representation of an uncertain fuzzy membership with a rough approximation. Based on our definition of R-fuzzy sets and their operations, the relationships between R-fuzzy sets and other fuzzy sets are discussed and some examples are provided

On intuitionistic fuzzy negations and intuitionistic fuzzy extended modal operators. Part 2.

On intuitionistic fuzzy negations and intuitionistic fuzzy extended modal operators. Part 2

Genetic evolution of sorting programs through a novel genotype-phenotype mapping

This paper presents an adaptable genetic evolutionary system, which includes an innovative approach to mapping genotypes to phenotypes through XML rules. The evolutionary system was originally created to evolve Regular Expressions (REs) to automate the extraction of web information. However, the system has been adapted to work with a completely different domain – Complete Software Programs – to demonstrate the flexibility of this approach. Specifically, the paper concentrates on the evolution of 'Sorting' programs . Experiments show that our evolutionary system is successful and can be adapted to work for challenging domains with minimum effort

Robot competence development by constructive learning

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system's adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use

An improved representation for evolving programs

A representation has been developed that addresses some of the issues with other Genetic Program representations while maintaining their advantages. This combines the easy reproduction of the linear representation with the inherita- ble characteristics of the tree representation by using fixed-length blocks of genes representing single program statements. This means that each block of genes will always map to the same statement in the parent and child unless it is mutated, irrespective of changes to the surrounding blocks. This method is compared to the variable length gene blocks used by other representations with a clear improvement in the similarity between parent and child. In addition, a set of list evaluation and manipulation functions was evolved as an application of the new Genetic Program components. These functions have the common feature that they all need to be 100% correct to be useful. Traditional Genetic Programming problems have mainly been optimization or approximation problems. The list results are good but do highlight the problem of scalability in that more complex functions lead to a dramatic increase in the required evolution time

An evolution of a complete program using XML-based grammar definition

XML technology is a technique to describe structured data that can be manipulated by different types of applications, especially to represent content on the Web. This paper presents a viable approach to automatically evolve a ‘sorting program’ by applying genetic programming and full syntax XML-based grammar definition to map the genotype to phenotype. The genotypes are composed of fixed-length blocks of genes that are made up of a series of integer values. The paper reports that our approach improves the structure of the grammar used in the mapping process, which guarantees that the generated program follows the correct syntax with no repair function, in comparison to earlier work. This allows more structured programs than earlier systems

A stepwise evolution of functions

A Genotype-Phenotype mapping in most Genetic Programming (GP) systems uses a predefined and rigid grammar definition. This method has been successful in producing the required solution. However, it can only be used to solve a limited set of problems. In this paper, a Teachable GP (TGP) system is proposed. An external GP system evolves a complete computer program, which acceptable solution is then added automatically to the existing grammar definition as a function and made available to the TGP system. This dynamic grammar definition allows for a more complex program to be generated, solving more complex problems. Experiments are performed to compare performances between GP without the added function, GP with a user-defined function and GP with the evolved function and results shows that GP with an evolved function is comparable to the GP with user-defined function and outperformed GP without function

Semantic transfer and contradictory evidence in intuitionistic fuzzy sets

The relationship between object level intuitionistic fuzzy sets and predicate based intuitionistic fuzzy sets is explored. Mass assignment uses a process called semantic unification to evaluate the degree to which one set supports another, the inverse function is semantic separation. Intuitionistic fuzzy sets are mapped onto a mass assignment framework and the semantic unification operator is generalised to support both mass assignment and intuitionistic fuzzy sets, as is semantic separation. Transfer of inconsistent and contradictory evidence are also dealt with