Social and Instrumental Privacy
When the definition of the situation is not clear, performances on SNS become difficult in relation to privacy on mainly two levels: social privacy and instrumental
privacy .
The former can be defined, according to Raynes-Goldie ( 2010 ) as “the control of
information fl ow about how and when their personal information is shared with
other people”. It usually deals with disclosure .
The latter refers to the access by governments and corporations to users data,
usually via data mining techniques (boyd and Hargittai 2010 ). Instrumental privacy
in online environments deals with the problem of not awareness of people about
what happens with their personal information, i.e., who and why they are gathered
and the possibility for users to do something about it. In this scenario, individuals
often lack every ability to act in a meaningful way (Solove 2001 ).
Disclosure and data mining in social network services are two macro areas
including several privacy issues. Concerning the former area, main topics are selfdisclosure
(Krasnovaet al. 2009 ), context collapse (boyd and Ellison 2007 ) or context
collision (Raynes-Goldie 2010 ), and forced disclosure (Gross and Acquisti
2005 ). Concerning the latter area, both emergent and well known topics are represented
by filter bubble (Pariser 2011 ) and link prediction (Lü and Zhou 2011 ). All
these issues in both areas refer to major gaps in the architecture of SNS. These
makes it hard for users to interact, represent themselves and create communities and
on top of that bear in mind their social and instrumental privacy.