1,434 research outputs found
Extended Combinatorial Constructions for Peer-to-peer User-Private Information Retrieval
We consider user-private information retrieval (UPIR), an interesting
alternative to private information retrieval (PIR) introduced by Domingo-Ferrer
et al. In UPIR, the database knows which records have been retrieved, but does
not know the identity of the query issuer. The goal of UPIR is to disguise user
profiles from the database. Domingo-Ferrer et al.\ focus on using a
peer-to-peer community to construct a UPIR scheme, which we term P2P UPIR. In
this paper, we establish a strengthened model for P2P UPIR and clarify the
privacy goals of such schemes using standard terminology from the field of
privacy research. In particular, we argue that any solution providing privacy
against the database should attempt to minimize any corresponding loss of
privacy against other users. We give an analysis of existing schemes, including
a new attack by the database. Finally, we introduce and analyze two new
protocols. Whereas previous work focuses on a special type of combinatorial
design known as a configuration, our protocols make use of more general
designs. This allows for flexibility in protocol set-up, allowing for a choice
between having a dynamic scheme (in which users are permitted to enter and
leave the system), or providing increased privacy against other users.Comment: Updated version, which reflects reviewer comments and includes
expanded explanations throughout. Paper is accepted for publication by
Advances in Mathematics of Communication
Octopus: A Secure and Anonymous DHT Lookup
Distributed Hash Table (DHT) lookup is a core technique in structured
peer-to-peer (P2P) networks. Its decentralized nature introduces security and
privacy vulnerabilities for applications built on top of them; we thus set out
to design a lookup mechanism achieving both security and anonymity, heretofore
an open problem. We present Octopus, a novel DHT lookup which provides strong
guarantees for both security and anonymity. Octopus uses attacker
identification mechanisms to discover and remove malicious nodes, severely
limiting an adversary's ability to carry out active attacks, and splits lookup
queries over separate anonymous paths and introduces dummy queries to achieve
high levels of anonymity. We analyze the security of Octopus by developing an
event-based simulator to show that the attacker discovery mechanisms can
rapidly identify malicious nodes with low error rate. We calculate the
anonymity of Octopus using probabilistic modeling and show that Octopus can
achieve near-optimal anonymity. We evaluate Octopus's efficiency on Planetlab
with 207 nodes and show that Octopus has reasonable lookup latency and
manageable communication overhead
A Comprehensive Analysis of Swarming-based Live Streaming to Leverage Client Heterogeneity
Due to missing IP multicast support on an Internet scale, over-the-top media
streams are delivered with the help of overlays as used by content delivery
networks and their peer-to-peer (P2P) extensions. In this context,
mesh/pull-based swarming plays an important role either as pure streaming
approach or in combination with tree/push mechanisms. However, the impact of
realistic client populations with heterogeneous resources is not yet fully
understood. In this technical report, we contribute to closing this gap by
mathematically analysing the most basic scheduling mechanisms latest deadline
first (LDF) and earliest deadline first (EDF) in a continuous time Markov chain
framework and combining them into a simple, yet powerful, mixed strategy to
leverage inherent differences in client resources. The main contributions are
twofold: (1) a mathematical framework for swarming on random graphs is proposed
with a focus on LDF and EDF strategies in heterogeneous scenarios; (2) a mixed
strategy, named SchedMix, is proposed that leverages peer heterogeneity. The
proposed strategy, SchedMix is shown to outperform the other two strategies
using different abstractions: a mean-field theoretic analysis of buffer
probabilities, simulations of a stochastic model on random graphs, and a
full-stack implementation of a P2P streaming system.Comment: Technical report and supplementary material to
http://ieeexplore.ieee.org/document/7497234
Building a privacy-preserving semantic overlay for Peer-to-Peer networks
Searching a Peer-to-Peer (P2P) network without using a central index has been widely investigated but proved to be very difficult. Various strategies have been proposed, however no practical solution to date also addresses privacy concerns. By clustering peers which have similar interests, a semantic overlay provides a method for achieving scalable search. Traditionally, in order to find similar peers, a peer is required to fully expose its preferences for items or content, therefore disclosing this private information. However, in a hostile environment, such as a P2P system, a peer can not know the true identity or intentions of fellow peers. In this paper, we propose two protocols for building a semantic overlay in a privacy-preserving manner by modifying existing solutions to the Private Set Intersection (PSI) problem. Peers in our overlay compute their similarity to other peers in the encrypted domain, allowing them to find similar peers. Using homomorphic encryption, peers can carrying out computations on encrypted values, without needing to decrypt them first. We propose two protocols, one based on the inner product of vectors, the other on multivariate polynomial evaluation, which are able to compute a similarity value between two peers. Both protocols are implemented on top of an existing P2P platform and are designed for actual deployment. Using a supercomputer and a dataset extracted from a real world instance of a semantic overlay, we emulate our protocols in a network consisting of a thousand peers. Finally, we show the actual computational and bandwidth usage of the protocols as recorded during those experiments
Cloud-based Content Distribution on a Budget
To leverage the elastic nature of cloud computing, a solution provider must be able to accurately gauge demand for its offering. For applications that involve swarm-to-cloud interactions, gauging such demand is not straightforward. In this paper, we propose a general framework, analyze a mathematical model, and present a prototype implementation of a canonical swarm-to-cloud application, namely peer-assisted content delivery. Our system – called Cyclops – dynamically adjusts the off-cloud bandwidth consumed by content servers (which represents the bulk of the provider's cost) to feed a set of swarming clients, based on a feedback signal that gauges the real-time health of the swarm. Our extensive evaluation of Cyclops in a variety of settings – including controlled PlanetLab and live Internet experiments involving thousands of users – show significant reduction in content distribution costs (by as much as two orders of magnitude) when compared to non-feedback-based swarming solutions, with minor impact on content delivery times
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