1,330 research outputs found
Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries
We propose an efficient framework for enabling secure multi-party numerical
computations in a Peer-to-Peer network. This problem arises in a range of
applications such as collaborative filtering, distributed computation of trust
and reputation, monitoring and other tasks, where the computing nodes is
expected to preserve the privacy of their inputs while performing a joint
computation of a certain function. Although there is a rich literature in the
field of distributed systems security concerning secure multi-party
computation, in practice it is hard to deploy those methods in very large scale
Peer-to-Peer networks. In this work, we try to bridge the gap between
theoretical algorithms in the security domain, and a practical Peer-to-Peer
deployment.
We consider two security models. The first is the semi-honest model where
peers correctly follow the protocol, but try to reveal private information. We
provide three possible schemes for secure multi-party numerical computation for
this model and identify a single light-weight scheme which outperforms the
others. Using extensive simulation results over real Internet topologies, we
demonstrate that our scheme is scalable to very large networks, with up to
millions of nodes. The second model we consider is the malicious peers model,
where peers can behave arbitrarily, deliberately trying to affect the results
of the computation as well as compromising the privacy of other peers. For this
model we provide a fourth scheme to defend the execution of the computation
against the malicious peers. The proposed scheme has a higher complexity
relative to the semi-honest model. Overall, we provide the Peer-to-Peer network
designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA)
200
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MobileTrust: Secure Knowledge Integration in VANETs
Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy
ARPA Whitepaper
We propose a secure computation solution for blockchain networks. The
correctness of computation is verifiable even under malicious majority
condition using information-theoretic Message Authentication Code (MAC), and
the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty
computation protocol and a layer2 solution, our privacy-preserving computation
guarantees data security on blockchain, cryptographically, while reducing the
heavy-lifting computation job to a few nodes. This breakthrough has several
implications on the future of decentralized networks. First, secure computation
can be used to support Private Smart Contracts, where consensus is reached
without exposing the information in the public contract. Second, it enables
data to be shared and used in trustless network, without disclosing the raw
data during data-at-use, where data ownership and data usage is safely
separated. Last but not least, computation and verification processes are
separated, which can be perceived as computational sharding, this effectively
makes the transaction processing speed linear to the number of participating
nodes. Our objective is to deploy our secure computation network as an layer2
solution to any blockchain system. Smart Contracts\cite{smartcontract} will be
used as bridge to link the blockchain and computation networks. Additionally,
they will be used as verifier to ensure that outsourced computation is
completed correctly. In order to achieve this, we first develop a general MPC
network with advanced features, such as: 1) Secure Computation, 2) Off-chain
Computation, 3) Verifiable Computation, and 4)Support dApps' needs like
privacy-preserving data exchange
McFIL: Model Counting Functionality-Inherent Leakage
Protecting the confidentiality of private data and using it for useful
collaboration have long been at odds. Modern cryptography is bridging this gap
through rapid growth in secure protocols such as multi-party computation,
fully-homomorphic encryption, and zero-knowledge proofs. However, even with
provable indistinguishability or zero-knowledgeness, confidentiality loss from
leakage inherent to the functionality may partially or even completely
compromise secret values without ever falsifying proofs of security. In this
work, we describe McFIL, an algorithmic approach and accompanying software
implementation which automatically quantifies intrinsic leakage for a given
functionality. Extending and generalizing the Chosen-Ciphertext attack
framework of Beck et al. with a practical heuristic, our approach not only
quantifies but maximizes functionality-inherent leakage using Maximum Model
Counting within a SAT solver. As a result, McFIL automatically derives
approximately-optimal adversary inputs that, when used in secure protocols,
maximize information leakage of private values.Comment: To appear in USENIX Security 202
Peer-to-Peer Secure Multi-Party Numerical Computation
We propose an efficient framework for enabling secure multi-party numerical
computations in a Peer-to-Peer network. This problem arises in a range of
applications such as collaborative filtering, distributed computation of trust
and reputation, monitoring and numerous other tasks, where the computing nodes
would like to preserve the privacy of their inputs while performing a joint
computation of a certain function.
Although there is a rich literature in the field of distributed systems
security concerning secure multi-party computation, in practice it is hard to
deploy those methods in very large scale Peer-to-Peer networks. In this work,
we examine several possible approaches and discuss their feasibility. Among the
possible approaches, we identify a single approach which is both scalable and
theoretically secure.
An additional novel contribution is that we show how to compute the
neighborhood based collaborative filtering, a state-of-the-art collaborative
filtering algorithm, winner of the Netflix progress prize of the year 2007. Our
solution computes this algorithm in a Peer-to-Peer network, using a privacy
preserving computation, without loss of accuracy.
Using extensive large scale simulations on top of real Internet topologies,
we demonstrate the applicability of our approach. As far as we know, we are the
first to implement such a large scale secure multi-party simulation of networks
of millions of nodes and hundreds of millions of edges.Comment: 10 pages, 2 figures, appeared in the 8th IEEE Peer-to-Peer Computing,
Aachen, Germany, Sept. 200
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