Abstract—Releasing social network data
could seriously breach user privacy. User profile and friendship relations are
inherently private. Unfortunately, it is possible to predict sensitive
information carried in released data latently by utilizing data mining techniques.
Therefore, sanitizing network data prior to release is necessary. In this
paper, we explore how to launch an inference attack exploiting social networks
with a mixture of non-sensitive attributes and social relationships. We map
this issue to a collective classification problem and propose a collective
inference model. In our model, an attacker utilizes user profile and social
relationships in a collective manner to predict sensitive information of
related victims in a released social network dataset. To protect against such
attacks, we propose a data sanitization method collectively manipulating user
profile and friendship relations. The key novel idea lies that besides
sanitizing friendship relations, the proposed method can take advantages of various
data-manipulating methods. We show that we can easily reduce adversary’s
prediction accuracy on sensitive information, while resulting in less accuracy
decrease on non-sensitive information towards three social network datasets. To
the best of our knowledge, this is the first work that employs collective
methods involving various data-manipulating methods and social relationships to
protect against inference attacks in social networks.
CONCLUSIONS
We address two issues in this paper: (a) how exactly third
party users launch an inference attack to predict sensitive information of
users, and (b) are there effective strategies to protect against such an attack
to achieve a desired privacyutility tradeoff. For the first issue, we show that
collectively utilizing both attribute and link information can significantly
increase prediction accuracy for sensitive information. For the second issue,
we explore the dependence relationships for Any Query Call Us:
9566355386
utility/public
attributes, and privacy/public attributes. Based on these results, we propose a
Collective Method that take advantages of various data manipulating methods to
guarantee sanitizing user data does not incur a bad impact on data utility.
Using Collective Method, we are able to effectively sanitize social network
data prior to release. The solutions for the two addressed issues are proven to
be effective towards three real social datasets.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
• System : Pentium IV
2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44
Mb.
• Monitor : 15 VGA
Colour.
• Mouse : Logitech.
• Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
• Operating system : -
Windows XP/7.
• Coding Language :
JAVA/J2EE
• Data Base : MYSQL
REFERENCES
[1] https://www.researchgate.net/.
[2] http://www.imdb.com/.
[3] “Facebook beacon,” 2007.