997 research outputs found
Implementing TontineCoin
One of the alternatives to proof-of-work (PoW) consensus protocols is proof-of- stake (PoS) protocols, which address its energy and cost related issues. But they suffer from the nothing-at-stake problem; validators (PoS miners) are bound to lose nothing if they support multiple blockchain forks. Tendermint, a PoS protocol, handles this problem by forcing validators to bond their stake and then seizing a cheater’s stake when caught signing multiple competing blocks. The seized stake is then evenly distributed amongst the rest of validators. However, as the number of validators increases, the benefit in finding a cheater compared to the cost of monitoring validators reduces, weakening the system’s defense against the problem. Previous work on TontineCoin addresses this problem by utilizing the concept of tontines. A tontine is an investment scheme in which each participant receives a portion of benefits based on their share. As the number of participants in a tontine decreases, individual benefit increases, which acts as a motivation for participants to eliminate each other. Utilizing this feature in TontineCoin ensures that validators (participants of a tontine) are highly motivated to monitor each other, thus strengthening the system against the nothing-at-stake problem. This project implements a prototype of Tendermint using the Spartan Gold codebase and develops TontineCoin based on it. This implementation is the first implementation of the protocol, and simulates and contrasts five different normal operations in both the Tendermint and TontineCoin models. It also simulates and discusses how a nothing-at-stake attack is handled in TontineCoin compared to Tendermint
Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids
Smart grid is a large complex network with a myriad of vulnerabilities,
usually operated in adversarial settings and regulated based on estimated
system states. In this study, we propose a novel highly secure distributed
dynamic state estimation mechanism for wide-area (multi-area) smart grids,
composed of geographically separated subregions, each supervised by a local
control center. We firstly propose a distributed state estimator assuming
regular system operation, that achieves near-optimal performance based on the
local Kalman filters and with the exchange of necessary information between
local centers. To enhance the security, we further propose to (i) protect the
network database and the network communication channels against attacks and
data manipulations via a blockchain (BC)-based system design, where the BC
operates on the peer-to-peer network of local centers, (ii) locally detect the
measurement anomalies in real-time to eliminate their effects on the state
estimation process, and (iii) detect misbehaving (hacked/faulty) local centers
in real-time via a distributed trust management scheme over the network. We
provide theoretical guarantees regarding the false alarm rates of the proposed
detection schemes, where the false alarms can be easily controlled. Numerical
studies illustrate that the proposed mechanism offers reliable state estimation
under regular system operation, timely and accurate detection of anomalies, and
good state recovery performance in case of anomalies
DFL: High-Performance Blockchain-Based Federated Learning
Many researchers are trying to replace the aggregation server in federated
learning with a blockchain system to achieve better privacy, robustness and
scalability. In this case, clients will upload their updated models to the
blockchain ledger, and use a smart contract on the blockchain system to perform
model averaging. However, running machine learning applications on the
blockchain is almost impossible because a blockchain system, which usually
takes over half minute to generate a block, is extremely slow and unable to
support machine learning applications.
This paper proposes a completely new public blockchain architecture called
DFL, which is specially optimized for distributed federated machine learning.
This architecture inherits most traditional blockchain merits and achieves
extremely high performance with low resource consumption by waiving global
consensus. To characterize the performance and robustness of our architecture,
we implement the architecture as a prototype and test it on a physical
four-node network. To test more nodes and more complex situations, we build a
simulator to simulate the network. The LeNet results indicate our system can
reach over 90% accuracy for non-I.I.D. datasets even while facing model
poisoning attacks, with the blockchain consuming less than 5% of hardware
resources.Comment: 11 pages, 17 figure
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
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
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