1,174 research outputs found
X-Vine: Secure and Pseudonymous Routing Using Social Networks
Distributed hash tables suffer from several security and privacy
vulnerabilities, including the problem of Sybil attacks. Existing social
network-based solutions to mitigate the Sybil attacks in DHT routing have a
high state requirement and do not provide an adequate level of privacy. For
instance, such techniques require a user to reveal their social network
contacts. We design X-Vine, a protection mechanism for distributed hash tables
that operates entirely by communicating over social network links. As with
traditional peer-to-peer systems, X-Vine provides robustness, scalability, and
a platform for innovation. The use of social network links for communication
helps protect participant privacy and adds a new dimension of trust absent from
previous designs. X-Vine is resilient to denial of service via Sybil attacks,
and in fact is the first Sybil defense that requires only a logarithmic amount
of state per node, making it suitable for large-scale and dynamic settings.
X-Vine also helps protect the privacy of users social network contacts and
keeps their IP addresses hidden from those outside of their social circle,
providing a basis for pseudonymous communication. We first evaluate our design
with analysis and simulations, using several real world large-scale social
networking topologies. We show that the constraints of X-Vine allow the
insertion of only a logarithmic number of Sybil identities per attack edge; we
show this mitigates the impact of malicious attacks while not affecting the
performance of honest nodes. Moreover, our algorithms are efficient, maintain
low stretch, and avoid hot spots in the network. We validate our design with a
PlanetLab implementation and a Facebook plugin.Comment: 15 page
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
Defending against Sybil Devices in Crowdsourced Mapping Services
Real-time crowdsourced maps such as Waze provide timely updates on traffic,
congestion, accidents and points of interest. In this paper, we demonstrate how
lack of strong location authentication allows creation of software-based {\em
Sybil devices} that expose crowdsourced map systems to a variety of security
and privacy attacks. Our experiments show that a single Sybil device with
limited resources can cause havoc on Waze, reporting false congestion and
accidents and automatically rerouting user traffic. More importantly, we
describe techniques to generate Sybil devices at scale, creating armies of
virtual vehicles capable of remotely tracking precise movements for large user
populations while avoiding detection. We propose a new approach to defend
against Sybil devices based on {\em co-location edges}, authenticated records
that attest to the one-time physical co-location of a pair of devices. Over
time, co-location edges combine to form large {\em proximity graphs} that
attest to physical interactions between devices, allowing scalable detection of
virtual vehicles. We demonstrate the efficacy of this approach using
large-scale simulations, and discuss how they can be used to dramatically
reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio
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