Technology is shaping the way people live, work, and interact with each other, and graduates of our computer science programs increasingly find themselves designing algorithms and using data that raise ethical issues they may not be aware of or equipped to address. Courses that contemplate the role of technology in society have been a standard, but often optional, part of curricula for years. An emerging alternative is to embed ethical discussions as modules within CS courses. This approach offers the opportunity to tie ethical issues to technical content at the moment students learn it, and to have students engage with these issues repeatedly throughout their degree. However, little is known about the effect of embedded ethics education on students.

This is a preliminary work to extend SQL to support user privacy in social networks. The proposal is to extend the data definition and data manipulation languages to capture privacy-preserved mandatory and discretionary access controls, respectively. Here, we focus on common user privacy requirements, such as purpose, generalization, and retention, used by social networks desiring to support privacy. Hence, each user can discretionarily control the set of privileges over the view representing their profile. We plan to support the extended language with underlying catalogues, algorithms, and prototypes. The objective is to develop a low-cost mechanism to preserve privacy in databases,with applications in social networks, e-health, e-business, e-government, etc.

Privacy is a leading concern for anyone that utilizes computing resources whether shopping on the Internet or visiting their doctor. Legislative acts require enterprises and data collectors to protect the privacy of their customers and data owners. Although privacy policy frameworks such as P3P assist data collectors in demonstrating their privacy policies to customers (i.e. publishing privacy policy on Web sites), insufficient research has been reported to help users visualize privacy policies. This paper presents a privacy policy visualization model based on the predicates of a privacy policy model. The key contribution is to provide a visualization model that facilitates understanding the policies for the data owners and provides the opportunity for the policy officers to better understand the designed policies. Finally, we demonstrate the model with a use case drawn from the policies of an online social network.

The large scale of linear systems of equations results in costly solving time. These systems usually have specific properties that can be used for designing fast algorithms. In addition, using parallel programming on distributed memory clusters enables us to get the results even faster. This work introduces a new fast parallel algorithm for solving systems with a strictly diagonally dominant three-band Toeplitz coefficient matrix. We call this new method the sewing method because the boundaries sew the adjacent subsystems together.