144 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
Automatically Generating a Large, Culture-Specific Blocklist for China
Internet censorship measurements rely on lists of websites to be tested, or
"block lists" that are curated by third parties. Unfortunately, many of these
lists are not public, and those that are tend to focus on a small group of
topics, leaving other types of sites and services untested. To increase and
diversify the set of sites on existing block lists, we use natural language
processing and search engines to automatically discover a much wider range of
websites that are censored in China. Using these techniques, we create a list
of 1125 websites outside the Alexa Top 1,000 that cover Chinese politics,
minority human rights organizations, oppressed religions, and more.
Importantly, . The list that we develop not only vastly expands the set
of sites that current Internet measurement tools can test, but it also deepens
our understanding of the nature of content that is censored in China. We have
released both this new block list and the code for generating it
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
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