HUMAN EFFECTS OF
ENHANCED PRIVACY MANAGEMENT MODELS
ABSTRACT:
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.
EXISTING SYSTEM:
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.
PROPOSED
SYSTEM:
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.
SYSTEM CONFIGURATION:-
HARDWARE REQUIREMENTS:-
Processor - Pentium –IV
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
SOFTWARE REQUIREMENTS:
Operating
system : Windows XP.
Coding
Language : JAVA
Data
Base : MySQL
Tool : Netbeans.
REFERENCE:
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.
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