Wednesday, 23 July 2014
Human Effects of Enhanced Privacy Management Models
HUMAN EFFECTS OF ENHANCED PRIVACY MANAGEMENT MODELS
We enhance existing and introduce new social network privacy management models and we measure their human effects. First, we introduce a mechanism using proven clustering techniques that assists users in grouping their friends for traditional groupbased policy management approaches. We found measurable agreement between clusters and user-defined relationship groups. Second, we introduce a new privacy management model that leverages users’ memory and opinion of their friends (called example friends) to set policies for other similar friends. Finally, we explore different techniques that aid users in selecting example friends. We found that by associating policy temples with example friends (versus group labels), users author policies more efficiently and have improved perceptions over traditional group-based policy management approaches. In addition, our results show that privacy management models can further enhanced by utilizing user privacy sentiment for mass customization. By detecting user privacy sentiment (i.e., an unconcerned user, a pragmatist or a fundamentalist), privacy management models can be automatically tailored specific to the privacy sentiment and needs of the user.
SOCIAL networking sites are experiencing tremendous adoption and growth. The Internet and online social networks, in particular, are a part of most people’s lives. eMarketer.com reports that in 2011, nearly 150 million US Internet users will interface with at least one social networking site per month. eMarketer.com also reports that in 2011, 90 percent of Internet users ages 18-24 and 82 percent of Internet users ages 25-34 will interact with at least one social networking site per month. This trend is increasing for all age groups. As the young population ages, they will continue to leverage social media in their daily lives. In addition, new generations will come to adopt the Internet and online social networks. These technologies have become and will continue to be a vital component of our social fabric, which we depend on to communicate, interact, and socialize.
DISADVANTAGES OF EXISTING SYSTEM:
v Large amount of content coupled with the significant
number of users online makes maintaining appropriate levels of privacy very challenging.
v Not efficient.
First, there are varying levels of privacy controls, depending on the online site. For example, some sites make available user profile data to the Internet with no ability to restrict access. While other sites limit user profile viewing to just trusted friends. Other studies introduce the notion of the privacy paradox, the relationship between individual privacy intentions to disclose their personal information and their actual behavior. Individuals voice concerns over the lack of adequate controls around their privacy information while freely providing their personal data. Other research concludes that individuals lack appropriate information to make informed privacy decisions. Moreover, when there is adequate information, short-term benefits are often opted over long-term privacy. However, contrary to common belief, people are concerned about privacy. But managing ones privacy can be challenging. This can be attributed to many things, for example, the lack of privacy controls available to the user, the complexity of using the controls, and the burden associated with managing these controls for large sets of users.
ADVANTAGES OF PROPOSED SYSTEM:
v An incremental improvement to traditional group-based policy management.
v Management—a new paradigm improvement over traditional group-based policy management.
v An incremental improvement to Same-As Policy Management.
Speed - 1.1 Ghz
RAM - 512 MB(min)
Hard Disk - 40 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - LCD/LED
Operating system : Windows XP.
Coding Language : JAVA
Data Base : MySQL
Tool : Netbeans.
Gorrell P. Cheek, and Mohamed Shehab, “Human Effects of Enhanced Privacy Management Models” IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL. 11, NO. 2, MARCH/APRIL 2014.