5,445 research outputs found
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
Using Metrics Suites to Improve the Measurement of Privacy in Graphs
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Social graphs are widely used in research (e.g., epidemiology) and business (e.g., recommender systems). However, sharing these graphs poses privacy risks because they contain sensitive information about individuals. Graph anonymization techniques aim to protect individual users in a graph, while graph de-anonymization aims to re-identify users. The effectiveness of anonymization and de-anonymization algorithms is usually evaluated with privacy metrics. However, it is unclear how strong existing privacy metrics are when they are used in graph privacy. In this paper, we study 26 privacy metrics for graph anonymization and de-anonymization and evaluate their strength in terms of three criteria: monotonicity indicates whether the metric indicates lower privacy for stronger adversaries; for within-scenario comparisons, evenness indicates whether metric values are spread evenly; and for between-scenario comparisons, shared value range indicates whether metrics use a consistent value range across scenarios. Our extensive experiments indicate that no single metric fulfills all three criteria perfectly. We therefore use methods from multi-criteria decision analysis to aggregate multiple metrics in a metrics suite, and we show that these metrics suites improve monotonicity compared to the best individual metric. This important result enables more monotonic, and thus more accurate, evaluations of new graph anonymization and de-anonymization algorithms
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
Quantification of De-anonymization Risks in Social Networks
The risks of publishing privacy-sensitive data have received considerable
attention recently. Several de-anonymization attacks have been proposed to
re-identify individuals even if data anonymization techniques were applied.
However, there is no theoretical quantification for relating the data utility
that is preserved by the anonymization techniques and the data vulnerability
against de-anonymization attacks.
In this paper, we theoretically analyze the de-anonymization attacks and
provide conditions on the utility of the anonymized data (denoted by anonymized
utility) to achieve successful de-anonymization. To the best of our knowledge,
this is the first work on quantifying the relationships between anonymized
utility and de-anonymization capability. Unlike previous work, our
quantification analysis requires no assumptions about the graph model, thus
providing a general theoretical guide for developing practical
de-anonymization/anonymization techniques.
Furthermore, we evaluate state-of-the-art de-anonymization attacks on a
real-world Facebook dataset to show the limitations of previous work. By
comparing these experimental results and the theoretically achievable
de-anonymization capability derived in our analysis, we further demonstrate the
ineffectiveness of previous de-anonymization attacks and the potential of more
powerful de-anonymization attacks in the future.Comment: Published in International Conference on Information Systems Security
and Privacy, 201
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
On the Measurement of Privacy as an Attacker's Estimation Error
A wide variety of privacy metrics have been proposed in the literature to
evaluate the level of protection offered by privacy enhancing-technologies.
Most of these metrics are specific to concrete systems and adversarial models,
and are difficult to generalize or translate to other contexts. Furthermore, a
better understanding of the relationships between the different privacy metrics
is needed to enable more grounded and systematic approach to measuring privacy,
as well as to assist systems designers in selecting the most appropriate metric
for a given application.
In this work we propose a theoretical framework for privacy-preserving
systems, endowed with a general definition of privacy in terms of the
estimation error incurred by an attacker who aims to disclose the private
information that the system is designed to conceal. We show that our framework
permits interpreting and comparing a number of well-known metrics under a
common perspective. The arguments behind these interpretations are based on
fundamental results related to the theories of information, probability and
Bayes decision.Comment: This paper has 18 pages and 17 figure
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