73 research outputs found
QuickSync: A Quickly Synchronizing PoS-Based Blockchain Protocol
To implement a blockchain, we need a blockchain protocol for all the nodes to
follow. To design a blockchain protocol, we need a block publisher selection
mechanism and a chain selection rule. In Proof-of-Stake (PoS) based blockchain
protocols, block publisher selection mechanism selects the node to publish the
next block based on the relative stake held by the node. However, PoS
protocols, such as Ouroboros v1, may face vulnerability to fully adaptive
corruptions.
In this paper, we propose a novel PoS-based blockchain protocol, QuickSync,
to achieve security against fully adaptive corruptions while improving on
performance. We propose a metric called block power, a value defined for each
block, derived from the output of the verifiable random function based on the
digital signature of the block publisher. With this metric, we compute chain
power, the sum of block powers of all the blocks comprising the chain, for all
the valid chains. These metrics are a function of the block publisher's stake
to enable the PoS aspect of the protocol. The chain selection rule selects the
chain with the highest chain power as the one to extend. This chain selection
rule hence determines the selected block publisher of the previous block. When
we use metrics to define the chain selection rule, it may lead to
vulnerabilities against Sybil attacks. QuickSync uses a Sybil attack resistant
function implemented using histogram matching. We prove that QuickSync
satisfies common prefix, chain growth, and chain quality properties and hence
it is secure. We also show that it is resilient to different types of
adversarial attack strategies. Our analysis demonstrates that QuickSync
performs better than Bitcoin by an order of magnitude on both transactions per
second and time to finality, and better than Ouroboros v1 by a factor of three
on time to finality
FNNC: Achieving Fairness through Neural Networks
In classification models fairness can be ensured by solving a constrained
optimization problem. We focus on fairness constraints like Disparate Impact,
Demographic Parity, and Equalized Odds, which are non-decomposable and
non-convex. Researchers define convex surrogates of the constraints and then
apply convex optimization frameworks to obtain fair classifiers. Surrogates
serve only as an upper bound to the actual constraints, and convexifying
fairness constraints might be challenging.
We propose a neural network-based framework, \emph{FNNC}, to achieve fairness
while maintaining high accuracy in classification. The above fairness
constraints are included in the loss using Lagrangian multipliers. We prove
bounds on generalization errors for the constrained losses which asymptotically
go to zero. The network is optimized using two-step mini-batch stochastic
gradient descent. Our experiments show that FNNC performs as good as the state
of the art, if not better. The experimental evidence supplements our
theoretical guarantees. In summary, we have an automated solution to achieve
fairness in classification, which is easily extendable to many fairness
constraints
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