25,325 research outputs found
A privacy-preserving, decentralized and functional Bitcoin e-voting protocol
Bitcoin, as a decentralized digital currency, has caused extensive research
interest. There are many studies based on related protocols on Bitcoin,
Bitcoin-based voting protocols also received attention in related literature.
In this paper, we propose a Bitcoin-based decentralized privacy-preserving
voting mechanism. It is assumed that there are n voters and m candidates. The
candidate who obtains t ballots can get x Bitcoins from each voter, namely nx
Bitcoins in total. We use a shuffling mechanism to protect voter's voting
privacy, at the same time, decentralized threshold signatures were used to
guarantee security and assign voting rights. The protocol can achieve
correctness, decentralization and privacy-preservings. By contrast with other
schemes, our protocol has a smaller number of transactions and can achieve a
more functional voting method.Comment: 5 pages;3 figures;Smartworld 201
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
A granular approach to source trustworthiness for negative trust assessment
The problem of determining what information to trust is crucial in many contexts that admit uncertainty and polarization. In this paper, we propose a method to systematically reason on the trustworthiness of sources. While not aiming at establishing their veracity, the metho
Keeping Authorities "Honest or Bust" with Decentralized Witness Cosigning
The secret keys of critical network authorities - such as time, name,
certificate, and software update services - represent high-value targets for
hackers, criminals, and spy agencies wishing to use these keys secretly to
compromise other hosts. To protect authorities and their clients proactively
from undetected exploits and misuse, we introduce CoSi, a scalable witness
cosigning protocol ensuring that every authoritative statement is validated and
publicly logged by a diverse group of witnesses before any client will accept
it. A statement S collectively signed by W witnesses assures clients that S has
been seen, and not immediately found erroneous, by those W observers. Even if S
is compromised in a fashion not readily detectable by the witnesses, CoSi still
guarantees S's exposure to public scrutiny, forcing secrecy-minded attackers to
risk that the compromise will soon be detected by one of the W witnesses.
Because clients can verify collective signatures efficiently without
communication, CoSi protects clients' privacy, and offers the first
transparency mechanism effective against persistent man-in-the-middle attackers
who control a victim's Internet access, the authority's secret key, and several
witnesses' secret keys. CoSi builds on existing cryptographic multisignature
methods, scaling them to support thousands of witnesses via signature
aggregation over efficient communication trees. A working prototype
demonstrates CoSi in the context of timestamping and logging authorities,
enabling groups of over 8,000 distributed witnesses to cosign authoritative
statements in under two seconds.Comment: 20 pages, 7 figure
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
How to Incentivize Data-Driven Collaboration Among Competing Parties
The availability of vast amounts of data is changing how we can make medical
discoveries, predict global market trends, save energy, and develop educational
strategies. In some settings such as Genome Wide Association Studies or deep
learning, sheer size of data seems critical. When data is held distributedly by
many parties, they must share it to reap its full benefits.
One obstacle to this revolution is the lack of willingness of different
parties to share data, due to reasons such as loss of privacy or competitive
edge. Cryptographic works address privacy aspects, but shed no light on
individual parties' losses/gains when access to data carries tangible rewards.
Even if it is clear that better overall conclusions can be drawn from
collaboration, are individual collaborators better off by collaborating?
Addressing this question is the topic of this paper.
* We formalize a model of n-party collaboration for computing functions over
private inputs in which participants receive their outputs in sequence, and the
order depends on their private inputs. Each output "improves" on preceding
outputs according to a score function.
* We say a mechanism for collaboration achieves collaborative equilibrium if
it ensures higher reward for all participants when collaborating (rather than
working alone). We show that in general, computing a collaborative equilibrium
is NP-complete, yet we design efficient algorithms to compute it in a range of
natural model settings.
Our collaboration mechanisms are in the standard model, and thus require a
central trusted party; however, we show this assumption is unnecessary under
standard cryptographic assumptions. We show how to implement the mechanisms in
a decentralized way with new extensions of secure multiparty computation that
impose order/timing constraints on output delivery to different players, as
well as privacy and correctness
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