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Fatimah Alashwali

Publications and source records attributed to Fatimah Alashwali.

2 recordsLinked to original sources

Saudi Parents' Privacy Concerns about Their Children's Smart Device Applications

In this paper, we investigate Saudi parents' privacy concerns regarding their children's smart device applications (apps). To this end, we conducted a survey and analysed 119 responses. Our results show that Saudi parents expressed a high level of concern regarding their children's privacy when using smart device apps. However, they expressed higher concerns about apps' content than privacy issues such as apps' requests to access sensitive data. Furthermore, parents' concerns are not in line with most of the children's installed apps, which contain apps inappropriate for their age, require parental guidance, and request access to sensitive data such as location. We also discuss several aspects of Saudi parents' practices and concerns compared to those reported by Western (mainly from the UK) and Chinese parents in previous reports. We found interesting patterns and established new relationships. For example, Saudi and Western parents show higher levels of privacy concerns than Chinese parents. Finally, we tested 14 privacy practices and concerns against high versus low socioeconomic classes (parents' education, technical background, and income) to find whether there are significant differences between high and low classes (we denote these differences by "digital divide"). Out of 42 tests (14 properties x 3 classes) we found significant differences between high and low classes in 7 tests only. While this is a positive trend overall, it is important to work on bridging these gaps. The results of this paper provide key findings to identify areas of improvement and recommendations, especially for Saudis, which can be used by parents, developers, researchers, regulators, and policy makers.

cs.CR

The use of a common location measure in the invariant coordinate selection and projection pursuit

Invariant coordinate selection (ICS) and projection pursuit (PP) are two methods that can be used to detect clustering directions in multivariate data by optimizing criteria sensitive to non-normality. In particular, ICS finds clustering directions using a relative eigen-decomposition of two scatter matrices with different levels of robustness; PP is a one-dimensional variant of ICS. Each of the two scatter matrices includes an implicit or explicit choice of location. However, when different measures of location are used, ICS and PP can behave counter-intuitively. In this paper we explore this behavior in a variety of examples and propose a simple and natural solution: use the same measure of location for both scatter matrices.

stat.ME