9,326 research outputs found
Private Multi-party Matrix Multiplication and Trust Computations
This paper deals with distributed matrix multiplication. Each player owns
only one row of both matrices and wishes to learn about one distinct row of the
product matrix, without revealing its input to the other players. We first
improve on a weighted average protocol, in order to securely compute a
dot-product with a quadratic volume of communications and linear number of
rounds. We also propose a protocol with five communication rounds, using a
Paillier-like underlying homomorphic public key cryptosystem, which is secure
in the semi-honest model or secure with high probability in the malicious
adversary model. Using ProVerif, a cryptographic protocol verification tool, we
are able to check the security of the protocol and provide a countermeasure for
each attack found by the tool. We also give a randomization method to avoid
collusion attacks. As an application, we show that this protocol enables a
distributed and secure evaluation of trust relationships in a network, for a
large class of trust evaluation schemes.Comment: Pierangela Samarati. SECRYPT 2016 : 13th International Conference on
Security and Cryptography, Lisbonne, Portugal, 26--28 Juillet 2016. 201
Trust-Based Mechanisms for Robust and Efficient Task Allocation in the Presence of Execution Uncertainty
Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent’s probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper, we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2×105 possible allocations in 40 seconds).
Matrix powers algorithms for trust evaluation in PKI architectures
This paper deals with the evaluation of trust in public-key infrastructures.
Different trust models have been proposed to interconnect the various PKI
components in order to propagate the trust between them. In this paper we
provide a new polynomial algorithm using linear algebra to assess trust
relationships in a network using different trust evaluation schemes. The
advantages are twofold: first the use of matrix computations instead of graph
algorithms provides an optimized computational solution; second, our algorithm
can be used for generic graphs, even in the presence of cycles. Our algorithm
is designed to evaluate the trust using all existing (finite) trust paths
between entities as a preliminary to any exchanges between PKIs. This can give
a precise evaluation of trust, and accelerate for instance cross-certificate
validation
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Vulnerabilities of Decentralized Additive Reputation Systems Regarding the Privacy of Individual Votes
In this paper, we focus on attacks and defense mechanisms in additive reputation systems. We start by surveying the most important protocols that aim to provide privacy between individual voters. Then, we categorize attacks against additive reputation systems considering both malicious querying nodes and malicious reporting nodes that collaborate in order to undermine the vote privacy of the remaining users. To the best of our knowledge this is the first work that provides a description of such malicious behavior against such systems. In light of this analysis we demonstrate the inefficiencies of existing protocols
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
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