A large amount of digital information collected and stored in datasets creates vast opportunities for knowledge discovery and data mining. These datasets, however, may contain sensitive information about individuals and, therefore, it is imperative to ensure that their privacy is protected. Most research in the area of privacy preserving data publishing does not make any assumptions about an intended analysis task applied on the dataset. In many domains such as healthcare, finance, etc; however, it is possible to identify the analysis task beforehand. Incorporating such knowledge of the ultimate analysis task may improve the quality of the anonymized data while protecting the privacy of individuals. Furthermore, the existing research which consider the ultimate analysis task (e.g., classification) is not suitable for high-dimensional data. We show that automatic feature selection (which is a well-known dimensionality reduction technique) can be utilized in order to consider both aspects of privacy and utility simultaneously. In doing so, we show that feature selection can enhance existing privacy preserving techniques addressing k-anonymity and differential privacy and protect privacy while reducing the amount of modifications applied to the dataset; hence, in most of the cases achieving higher utility. We consider incorporating the concept of privacy-by-design within the feature selection process. We propose techniques that turn filter-based and wrapper-based feature selection into privacy-aware processes. To this end, we build a layer of privacy on top of regular feature selection process and obtain a privacy preserving feature selection that is not only guided by accuracy but also the amount of protected private information. In addition to considering privacy after feature selection we introduce a framework for a privacy-aware feature selection evaluation measure. That is, we incorporate privacy during feature selection and obtain a list of candidate privacy-aware attribute subsets that consider (and satisfy) both efficacy and privacy requirements simultaneously. Finally, we propose a multi-dimensional, privacy-aware evaluation function which incorporates efficacy, privacy, and dimensionality weights and enables the data holder to obtain a best attribute subset according to its preferences.

Food is one of the main routes by which xenobiotic (synthetic) chemicals enter the body of man and wildlife. The routes could be from wrappers in which the foods are presented with possible transfer of the compounds to consumers, hence need for regular screening. The research work is aimed at investigating possible prevalence of phthalate esters in selected foods (cheese, polony and vienna) and their plastic wrappers from commercial stores in Tshwane metropolis. Food samples were purchased from selected stores, taken to the laboratory and stored at 4oC until analysed. Analysis was done by soxhlet extraction while determination and quantification of phthalates was carried out using Gas Chromatography-Flame Ionization Detection (GC-FID). Quality assurance of the process was by standard addition of the phthalate ester standards. Results obtained revealed good chromatographic separation of the analysed esters which ranged from 5.55 min for Dimethyl phthalate (DMP) to 8.96 min for Benzylbutyl phthalate (BBP). Instrumental detection limit of the esters varied from 0.03 - 0.05 μg/kg. The percentage recovery of the phthalate esters ranged from 75 – 90% from spiked cheese samples; 33 – 66% from spiked polony samples and 69 – 99% from spiked vienna samples. These recoveries are quite acceptable and applicable to the analysis and quantification of the compounds in the samples with the exception of Dibutyl phthalate (DBP) (33%); DMP (34%) and BBP (46 %) in polony samples. Results from chromatographic quantification revealed the absence of or non-detection of most of the analysed phthalate esters in the selected food samples. However, level of 0.031 μg/kg of BBP - 0.816 μg/kg of DMP were obtained in some of the analysed samples. / Environmental Sciences / M.Sc. (Environmental Science)

Food is one of the main routes by which xenobiotic (synthetic) chemicals enter the body of man and wildlife. The routes could be from wrappers in which the foods are presented with possible transfer of the compounds to consumers, hence need for regular screening. The research work is aimed at investigating possible prevalence of phthalate esters in selected foods (cheese, polony and vienna) and their plastic wrappers from commercial stores in Tshwane metropolis. Food samples were purchased from selected stores, taken to the laboratory and stored at 4oC until analysed. Analysis was done by soxhlet extraction while determination and quantification of phthalates was carried out using Gas Chromatography-Flame Ionization Detection (GC-FID). Quality assurance of the process was by standard addition of the phthalate ester standards. Results obtained revealed good chromatographic separation of the analysed esters which ranged from 5.55 min for Dimethyl phthalate (DMP) to 8.96 min for Benzylbutyl phthalate (BBP). Instrumental detection limit of the esters varied from 0.03 - 0.05 μg/kg. The percentage recovery of the phthalate esters ranged from 75 – 90% from spiked cheese samples; 33 – 66% from spiked polony samples and 69 – 99% from spiked vienna samples. These recoveries are quite acceptable and applicable to the analysis and quantification of the compounds in the samples with the exception of Dibutyl phthalate (DBP) (33%); DMP (34%) and BBP (46 %) in polony samples. Results from chromatographic quantification revealed the absence of or non-detection of most of the analysed phthalate esters in the selected food samples. However, level of 0.031 μg/kg of BBP - 0.816 μg/kg of DMP were obtained in some of the analysed samples. / Environmental Sciences / M. Sc. (Environmental Science)