Kambiz Ghazinour

Roba Darwish, Kambiz Ghazinour

Online Social Networking Sites attracted a massive number of users over the past decade but also raised privacy concerns with the amount of personal information disclosed. Studies have shown that 25% of the users are not aware of privacy settings provided by these sites or do not know how to change them. This paper investigates an approach towards understanding users' privacy behavior on social media, e.g. Facebook, through studying faces, tags and photo privacy settings. It classifies users based on their privacy selections and proposes a system for monitoring and recommending stronger privacy settings. An application is developed, and our case study examines the effectiveness of our model.

Tahani Albalawi, Kambiz Ghazinour, Austin Melton

Security models have been designed to ensure data is accessed and used in proper manner according to the security policies. Unfortunately, human role in designing security models has been ignored. Human behavior relates to many security breaches and plays a significant part in many security situations.In this paper, we study users' security decision making toward security and usability through the mental model approach. To elicit and depict users' security and usability mental models, crowd sourcing techniques and cognitive map method are applied and we have performed an experiment to evaluate our findings using Amazon MTurk.

Kambiz Ghazinour, Stan Matwin, Marina Sokolova

Ensuring privacy of users of social networks is probably an unsolvable conundrum. At the same time, an informed use of the existing privacy options by the social network participants may alleviate - or even prevent - some of the more drastic privacy-averse incidents. Unfortunately, recent surveys show that an average user is either not aware of these options or does not use them, probably due to their perceived complexity. It is therefore reasonable to believe that tools assisting users with two tasks: 1) understanding their social net behavior in terms of their privacy settings and broad privacy categories, and 2)recommending reasonable privacy options, will be a valuable tool for everyday privacy practice in a social network context. This paper presents YourPrivacyProtector, a recommender system that shows how simple machine learning techniques may provide useful assistance in these two tasks to Facebook users. We support our claim with empirical results of application of YourPrivacyProtector to two groups of Facebook users.

Kambiz Ghazinour, Mehdi Ghayoumi

In large databases, creating user interface for browsing or performing insertion, deletion or modification of data is very costly in terms of programming. In addition, each modification of an access control policy causes many potential and unpredictable side effects which cause rule conflicts or security breaches that affect the corresponding user interfaces as well. While changes to access control policies in databases are inevitable, having a dynamic system that generates interface according to the latest access control policies become increasingly valuable. Lack of such a system leads to unauthorized access to data and eventually violates the privacy of data owners. In this work, we discuss a dynamic interface that applies Role Based Access Control (RBAC) policies as the output of policy analysis and limits the amount of information that users have access according to the policies defined for roles. This interface also shows security administrators the effect of their changes from the user's point of view while minimizing the cost by generating the interface automatically.