88,104 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
Why the Economics Profession Must Actively Participate in the Privacy Protection Debate
When Google or the U.S. Census Bureau publish detailed statistics on browsing habits or neighborhood characteristics, some privacy is lost for everybody while supplying public information. To date, economists have not focused on the privacy loss inherent in data publication. In their stead, these issues have been advanced almost exclusively by computer scientists who are primarily interested in technical problems associated with protecting privacy. Economists should join the discussion, first, to determine where to balance privacy protection against data quality; a social choice problem. Furthermore, economists must ensure new privacy models preserve the validity of public data for economic research
Biosecurity: A 21st Century Challenge
Based on a review of key reports and experts' opinions, summarizes the debate over "dual-use" technologies and the various approaches to controlling biosecurity risk. Outlines proposed preventive measures and steps to build response capacity
Differentially Private Publication of Sparse Data
The problem of privately releasing data is to provide a version of a dataset
without revealing sensitive information about the individuals who contribute to
the data. The model of differential privacy allows such private release while
providing strong guarantees on the output. A basic mechanism achieves
differential privacy by adding noise to the frequency counts in the contingency
tables (or, a subset of the count data cube) derived from the dataset. However,
when the dataset is sparse in its underlying space, as is the case for most
multi-attribute relations, then the effect of adding noise is to vastly
increase the size of the published data: it implicitly creates a huge number of
dummy data points to mask the true data, making it almost impossible to work
with.
We present techniques to overcome this roadblock and allow efficient private
release of sparse data, while maintaining the guarantees of differential
privacy. Our approach is to release a compact summary of the noisy data.
Generating the noisy data and then summarizing it would still be very costly,
so we show how to shortcut this step, and instead directly generate the summary
from the input data, without materializing the vast intermediate noisy data. We
instantiate this outline for a variety of sampling and filtering methods, and
show how to use the resulting summary for approximate, private, query
answering. Our experimental study shows that this is an effective, practical
solution, with comparable and occasionally improved utility over the costly
materialization approach
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