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Understanding privacy leakage concerns in Facebook: A longitudinal case study
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThis thesis focuses on examining users’ perceptions of privacy leakage in Facebook – the world’s largest and most popular social network site (SNS). The global popularity of this SNS offers a hugely tempting resource for organisations engaged in online business. The personal data willingly shared between online friends’ networks intuitively appears to be a natural extension of current advertising strategies such as word-of-mouth and viral marketing. Therefore organisations are increasingly adopting innovative ways to exploit the detail-rich personal data of SNS users for business marketing. However, commercial use of such personal information has provoked outrage amongst Facebook users and has radically highlighted the issue of privacy leakage. To date, little is known about how SNS users perceive such leakage of privacy. So a greater understanding of the form and nature of SNS users’ concerns about privacy leakage would contribute to the current literature as well as help to formulate best practice guidelines for organisations.
Given the fluid, context-dependent and temporal nature of privacy, a longitudinal case study representing the launch of Facebook’s social Ads programme was conducted to investigate the phenomenon of privacy leakage within its real-life setting. A qualitative user blogs commentary was collected between November 2007 and December 2010 during the two-stage launch of the social Ads programme. Grounded theory data analysis procedures were used to analyse users’ blog postings. The resulting taxonomy shows that business integrity, user control, transparency, data protection breaches, automatic information broadcast and information leak are the core privacy leakage concerns of Facebook users. Privacy leakage concerns suggest three limits, or levels: organisational, user and legal, which provide the basis to understanding the nature and scope of the exploitation of SNS users’ data for commercial purposes. The case study reported herein is novel, as existing empirical research has not identified and analysed privacy leakage concerns of Facebook users
Online Privacy as a Collective Phenomenon
The problem of online privacy is often reduced to individual decisions to
hide or reveal personal information in online social networks (OSNs). However,
with the increasing use of OSNs, it becomes more important to understand the
role of the social network in disclosing personal information that a user has
not revealed voluntarily: How much of our private information do our friends
disclose about us, and how much of our privacy is lost simply because of online
social interaction? Without strong technical effort, an OSN may be able to
exploit the assortativity of human private features, this way constructing
shadow profiles with information that users chose not to share. Furthermore,
because many users share their phone and email contact lists, this allows an
OSN to create full shadow profiles for people who do not even have an account
for this OSN.
We empirically test the feasibility of constructing shadow profiles of sexual
orientation for users and non-users, using data from more than 3 Million
accounts of a single OSN. We quantify a lower bound for the predictive power
derived from the social network of a user, to demonstrate how the
predictability of sexual orientation increases with the size of this network
and the tendency to share personal information. This allows us to define a
privacy leak factor that links individual privacy loss with the decision of
other individuals to disclose information. Our statistical analysis reveals
that some individuals are at a higher risk of privacy loss, as prediction
accuracy increases for users with a larger and more homogeneous first- and
second-order neighborhood of their social network. While we do not provide
evidence that shadow profiles exist at all, our results show that disclosing of
private information is not restricted to an individual choice, but becomes a
collective decision that has implications for policy and privacy regulation
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
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