1,680 research outputs found
Anonymizing Social Graphs via Uncertainty Semantics
Rather than anonymizing social graphs by generalizing them to super
nodes/edges or adding/removing nodes and edges to satisfy given privacy
parameters, recent methods exploit the semantics of uncertain graphs to achieve
privacy protection of participating entities and their relationship. These
techniques anonymize a deterministic graph by converting it into an uncertain
form. In this paper, we propose a generalized obfuscation model based on
uncertain adjacency matrices that keep expected node degrees equal to those in
the unanonymized graph. We analyze two recently proposed schemes and show their
fitting into the model. We also point out disadvantages in each method and
present several elegant techniques to fill the gap between them. Finally, to
support fair comparisons, we develop a new tradeoff quantifying framework by
leveraging the concept of incorrectness in location privacy research.
Experiments on large social graphs demonstrate the effectiveness of our
schemes
Injecting Uncertainty in Graphs for Identity Obfuscation
Data collected nowadays by social-networking applications create fascinating
opportunities for building novel services, as well as expanding our
understanding about social structures and their dynamics. Unfortunately,
publishing social-network graphs is considered an ill-advised practice due to
privacy concerns. To alleviate this problem, several anonymization methods have
been proposed, aiming at reducing the risk of a privacy breach on the published
data, while still allowing to analyze them and draw relevant conclusions. In
this paper we introduce a new anonymization approach that is based on injecting
uncertainty in social graphs and publishing the resulting uncertain graphs.
While existing approaches obfuscate graph data by adding or removing edges
entirely, we propose using a finer-grained perturbation that adds or removes
edges partially: this way we can achieve the same desired level of obfuscation
with smaller changes in the data, thus maintaining higher utility. Our
experiments on real-world networks confirm that at the same level of identity
obfuscation our method provides higher usefulness than existing randomized
methods that publish standard graphs.Comment: VLDB201
Preserving Link Privacy in Social Network Based Systems
A growing body of research leverages social network based trust relationships
to improve the functionality of the system. However, these systems expose
users' trust relationships, which is considered sensitive information in
today's society, to an adversary.
In this work, we make the following contributions. First, we propose an
algorithm that perturbs the structure of a social graph in order to provide
link privacy, at the cost of slight reduction in the utility of the social
graph. Second we define general metrics for characterizing the utility and
privacy of perturbed graphs. Third, we evaluate the utility and privacy of our
proposed algorithm using real world social graphs. Finally, we demonstrate the
applicability of our perturbation algorithm on a broad range of secure systems,
including Sybil defenses and secure routing.Comment: 16 pages, 15 figure
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