2026-09-22T19:20:22Zhttps://ruor.uottawa.ca/server/oai/request

oai:ruor.uottawa.ca:10393/343202024-02-23T08:54:17Zcom_10393_242col_10393_11105

Jafer, Yasser Matwin, Stanislaw Sokolova, Marina 2016-02-25T19:56:45Z 2016-02-25T19:56:45Z 2016 http://hdl.handle.net/10393/34320 http://dx.doi.org/10.20381/ruor-5180 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. en Université d'Ottawa / University of Ottawa Task Oriented Privacy (TOP) Privacy Data Mining Data Publishing Feature Selection Privacy-aware Wrappers Privacy-aware Filters Privacy-aware Evaluation Measure for Feature Selection Task Oriented Privacy-preserving (TOP) Technologies Using Automatic Feature Selection Thesis PhD Doctoral Génie / Engineering Science informatique et génie électrique / Electrical Engineering and Computer Science open.access