19,676 research outputs found
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
A Critical Look at Decentralized Personal Data Architectures
While the Internet was conceived as a decentralized network, the most widely
used web applications today tend toward centralization. Control increasingly
rests with centralized service providers who, as a consequence, have also
amassed unprecedented amounts of data about the behaviors and personalities of
individuals.
Developers, regulators, and consumer advocates have looked to alternative
decentralized architectures as the natural response to threats posed by these
centralized services. The result has been a great variety of solutions that
include personal data stores (PDS), infomediaries, Vendor Relationship
Management (VRM) systems, and federated and distributed social networks. And
yet, for all these efforts, decentralized personal data architectures have seen
little adoption.
This position paper attempts to account for these failures, challenging the
accepted wisdom in the web community on the feasibility and desirability of
these approaches. We start with a historical discussion of the development of
various categories of decentralized personal data architectures. Then we survey
the main ideas to illustrate the common themes among these efforts. We tease
apart the design characteristics of these systems from the social values that
they (are intended to) promote. We use this understanding to point out numerous
drawbacks of the decentralization paradigm, some inherent and others
incidental. We end with recommendations for designers of these systems for
working towards goals that are achievable, but perhaps more limited in scope
and ambition
How Far Removed Are You? Scalable Privacy-Preserving Estimation of Social Path Length with Social PaL
Social relationships are a natural basis on which humans make trust
decisions. Online Social Networks (OSNs) are increasingly often used to let
users base trust decisions on the existence and the strength of social
relationships. While most OSNs allow users to discover the length of the social
path to other users, they do so in a centralized way, thus requiring them to
rely on the service provider and reveal their interest in each other. This
paper presents Social PaL, a system supporting the privacy-preserving discovery
of arbitrary-length social paths between any two social network users. We
overcome the bootstrapping problem encountered in all related prior work,
demonstrating that Social PaL allows its users to find all paths of length two
and to discover a significant fraction of longer paths, even when only a small
fraction of OSN users is in the Social PaL system - e.g., discovering 70% of
all paths with only 40% of the users. We implement Social PaL using a scalable
server-side architecture and a modular Android client library, allowing
developers to seamlessly integrate it into their apps.Comment: A preliminary version of this paper appears in ACM WiSec 2015. This
is the full versio
Ubiquitous Social Networks: Opportunities and Challenges for Privacy-Aware User Modelling
Privacy has been recognized as an important topic in the Internet for a long time, and technological developments in the area of privacy tools are ongoing. However, their focus was mainly on the individual. With the proliferation of social network sites, it has become more evident that the problem of privacy is not bounded by the perimeters of individuals but also by the privacy needs of their social networks. The objective of this paper is to contribute to the discussion about privacy in social network sites, a topic which we consider to be severely under-researched. We propose a framework for analyzing privacy requirements and for analyzing privacy-related data. We outline a combination of requirements analysis, conflict-resolution techniques, and a P3P extension that can contribute to privacy within such sites.World Wide Web, privacy, social network analysis, requirements analysis, privacy negotiation, ubiquity, P3P
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