3,281 research outputs found
ODIN: Obfuscation-based privacy-preserving consensus algorithm for Decentralized Information fusion in smart device Networks
The large spread of sensors and smart devices in urban infrastructures are motivating research in the area of the Internet of Things (IoT) to develop new services and improve citizensâ quality of life. Sensors and smart devices generate large amounts of measurement data from sensing the environment, which is used to enable services such as control of power consumption or traffic density. To deal with such a large amount of information and provide accurate measurements, service providers can adopt information fusion, which given the decentralized nature of urban deployments can be performed by means of consensus algorithms. These algorithms allow distributed agents to (iteratively) compute linear functions on the exchanged data, and take decisions based on the outcome, without the need for the support of a central entity. However, the use of consensus algorithms raises several security concerns, especially when private or security critical information is involved in the computation.
In this article we propose ODIN, a novel algorithm allowing information fusion over encrypted data. ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, which prevents distributed agents from having direct access to the data while they iteratively reach consensus; agents cannot access even the final consensus value but can only retrieve partial information (e.g., a binary decision). ODIN uses efficient additive obfuscation and proxy re-encryption during the update steps and garbled circuits to make final decisions on the obfuscated consensus. We discuss the security of our proposal and show its practicability and efficiency on real-world resource-constrained devices, developing a prototype implementation for Raspberry Pi devices
ODIN: Obfuscation-based privacy-preserving consensus algorithm for Decentralized Information fusion in smart device Networks
The large spread of sensors and smart devices in urban infrastructures are motivating research in the area of the Internet of Things (IoT) to develop new services and improve citizensâ quality of life. Sensors and smart devices generate large amounts of measurement data from sensing the environment, which is used to enable services such as control of power consumption or traffic density. To deal with such a large amount of information and provide accurate measurements, service providers can adopt information fusion, which given the decentralized nature of urban deployments can be performed by means of consensus algorithms. These algorithms allow distributed agents to (iteratively) compute linear functions on the exchanged data, and take decisions based on the outcome, without the need for the support of a central entity. However, the use of consensus algorithms raises several security concerns, especially when private or security critical information is involved in the computation.
In this article we propose ODIN, a novel algorithm allowing information fusion over encrypted data. ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, which prevents distributed agents from having direct access to the data while they iteratively reach consensus; agents cannot access even the final consensus value but can only retrieve partial information (e.g., a binary decision). ODIN uses efficient additive obfuscation and proxy re-encryption during the update steps and garbled circuits to make final decisions on the obfuscated consensus. We discuss the security of our proposal and show its practicability and efficiency on real-world resource-constrained devices, developing a prototype implementation for Raspberry Pi devices
Finite-time Convergent Gossiping
Gossip algorithms are widely used in modern distributed systems, with
applications ranging from sensor networks and peer-to-peer networks to mobile
vehicle networks and social networks. A tremendous research effort has been
devoted to analyzing and improving the asymptotic rate of convergence for
gossip algorithms. In this work we study finite-time convergence of
deterministic gossiping. We show that there exists a symmetric gossip algorithm
that converges in finite time if and only if the number of network nodes is a
power of two, while there always exists an asymmetric gossip algorithm with
finite-time convergence, independent of the number of nodes. For nodes,
we prove that a fastest convergence can be reached in node
updates via symmetric gossiping. On the other hand, under asymmetric gossip
among nodes with , it takes at least node
updates for achieving finite-time convergence. It is also shown that the
existence of finite-time convergent gossiping often imposes strong structural
requirements on the underlying interaction graph. Finally, we apply our results
to gossip algorithms in quantum networks, where the goal is to control the
state of a quantum system via pairwise interactions. We show that finite-time
convergence is never possible for such systems.Comment: IEEE/ACM Transactions on Networking, In Pres
LUNES: Agent-based Simulation of P2P Systems (Extended Version)
We present LUNES, an agent-based Large Unstructured NEtwork Simulator, which
allows to simulate complex networks composed of a high number of nodes. LUNES
is modular, since it splits the three phases of network topology creation,
protocol simulation and performance evaluation. This permits to easily
integrate external software tools into the main software architecture. The
simulation of the interaction protocols among network nodes is performed via a
simulation middleware that supports both the sequential and the
parallel/distributed simulation approaches. In the latter case, a specific
mechanism for the communication overhead-reduction is used; this guarantees
high levels of performance and scalability. To demonstrate the efficiency of
LUNES, we test the simulator with gossip protocols executed on top of networks
(representing peer-to-peer overlays), generated with different topologies.
Results demonstrate the effectiveness of the proposed approach.Comment: Proceedings of the International Workshop on Modeling and Simulation
of Peer-to-Peer Architectures and Systems (MOSPAS 2011). As part of the 2011
International Conference on High Performance Computing and Simulation (HPCS
2011
On the Necessary Memory to Compute the Plurality in Multi-Agent Systems
We consider the Relative-Majority Problem (also known as Plurality), in
which, given a multi-agent system where each agent is initially provided an
input value out of a set of possible ones, each agent is required to
eventually compute the input value with the highest frequency in the initial
configuration. We consider the problem in the general Population Protocols
model in which, given an underlying undirected connected graph whose nodes
represent the agents, edges are selected by a globally fair scheduler.
The state complexity that is required for solving the Plurality Problem
(i.e., the minimum number of memory states that each agent needs to have in
order to solve the problem), has been a long-standing open problem. The best
protocol so far for the general multi-valued case requires polynomial memory:
Salehkaleybar et al. (2015) devised a protocol that solves the problem by
employing states per agent, and they conjectured their upper bound
to be optimal. On the other hand, under the strong assumption that agents
initially agree on a total ordering of the initial input values, Gasieniec et
al. (2017), provided an elegant logarithmic-memory plurality protocol.
In this work, we refute Salehkaleybar et al.'s conjecture, by providing a
plurality protocol which employs states per agent. Central to our
result is an ordering protocol which allows to leverage on the plurality
protocol by Gasieniec et al., of independent interest. We also provide a
-state lower bound on the necessary memory to solve the problem,
proving that the Plurality Problem cannot be solved within the mere memory
necessary to encode the output.Comment: 14 pages, accepted at CIAC 201
Highly intensive data dissemination in complex networks
This paper presents a study on data dissemination in unstructured
Peer-to-Peer (P2P) network overlays. The absence of a structure in unstructured
overlays eases the network management, at the cost of non-optimal mechanisms to
spread messages in the network. Thus, dissemination schemes must be employed
that allow covering a large portion of the network with a high probability
(e.g.~gossip based approaches). We identify principal metrics, provide a
theoretical model and perform the assessment evaluation using a high
performance simulator that is based on a parallel and distributed architecture.
A main point of this study is that our simulation model considers
implementation technical details, such as the use of caching and Time To Live
(TTL) in message dissemination, that are usually neglected in simulations, due
to the additional overhead they cause. Outcomes confirm that these technical
details have an important influence on the performance of dissemination schemes
and that the studied schemes are quite effective to spread information in P2P
overlay networks, whatever their topology. Moreover, the practical usage of
such dissemination mechanisms requires a fine tuning of many parameters, the
choice between different network topologies and the assessment of behaviors
such as free riding. All this can be done only using efficient simulation tools
to support both the network design phase and, in some cases, at runtime
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