Cross-chain between a Parent Chain and Multiple Side Chains

In certain Blockchain systems, multiple Blockchains are required to operate cooperatively for security, performance, and capacity considerations. This invention defines a cross-chain mechanism where a main Blockchain issues the tokens, which can then be transferred and used in multiple side Blockchains to drive their operations. A set of witnesses are created to securely manage the token exchange across the main chain and multiple side chains. The system decouples the consensus algorithms between the main chain and side chains. We also discuss the coexistence of the main tokens and the native tokens in the side chains.Comment: 14 pages, 9 figure

How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers

As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale. However, privacy concerns arise due to the potential leakage of sensitive information from the training data. Recent research has revealed that deep learning models are vulnerable to various privacy attacks, including membership inference attacks, attribute inference attacks, and gradient inversion attacks. Notably, the efficacy of these attacks varies from model to model. In this paper, we answer a fundamental question: Does model architecture affect model privacy? By investigating representative model architectures from CNNs to Transformers, we demonstrate that Transformers generally exhibit higher vulnerability to privacy attacks compared to CNNs. Additionally, We identify the micro design of activation layers, stem layers, and LN layers, as major factors contributing to the resilience of CNNs against privacy attacks, while the presence of attention modules is another main factor that exacerbates the privacy vulnerability of Transformers. Our discovery reveals valuable insights for deep learning models to defend against privacy attacks and inspires the research community to develop privacy-friendly model architectures.Comment: Under revie

Dataset Obfuscation: Its Applications to and Impacts on Edge Machine Learning

Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties for edge applications. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights - in terms of the model accuracy, Frobenius-norm (F-norm)-based model distance, and level of data privacy - and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a trade-off between the model accuracy and privacy level and a trade-off between the model difference and privacy level. The results indicate broad application prospects for training outsourcing in edge computing and guarding against attacks in Federated Learning among edge devices.Comment: 6 page

A Pattern Language for Blockchain Governance

Blockchain technology has been used to build next-generation applications taking advantage of its decentralised nature. Nevertheless, there are some serious concerns about the trustworthiness of blockchain due to the vulnerabilities in on-chain algorithmic mechanisms, and tedious disputes and debates in off-chain communities. Accordingly, blockchain governance has received great attention for improving the trustworthiness of all decisions that direct a blockchain platform. However, there is a lack of systematic knowledge to guide practitioners to perform blockchain governance. We have performed a systematic literature review to understand the state-of-the-art of blockchain governance. We identify the lifecycle stages of a blockchain platform, and present 14 architectural patterns for blockchain governance in this study. This pattern language can provide guidance for the effective use of patterns for blockchain governance in practice, and support the architecture design of governance-driven blockchain systems

Projecting terrestrial carbon sequestration of the southeastern United States in the 21st century

How terrestrial ecosystems respond to future environmental change in the 21st century is critically important for understanding the feedbacks of terrestrial ecosystems to global climate change. The southeastern United States (SEUS) has been one of the major regions acting as a carbon sink over the past century; yet it is unclear how its terrestrial ecosystems will respond to global environmental change in the 21st century. Applying a process-based ecosystem model (Dynamic Land Ecosystem Model, DLEM) in combination with three projected climate change scenarios (A1B, A2, and B1 from the IPCC report) and changes in atmospheric carbon dioxide, nitrogen deposition, and ozone pollution, we examined the potential changes of carbon storage and fluxes in the terrestrial ecosystems across the SEUS during 2000–2099. Simulation results indicate that SEUS\u27s terrestrial ecosystems will likely continue to sequester carbon in the 21st century, resulting in an increase in total carbon density (i.e., litter, vegetation biomass and soil carbon) from 13.5 kg C/m2 in the 2000s to 16.8 kg C/m2 in the 2090s. The terrestrial gross primary production and net primary production will probably continuously increase, while the net carbon exchange (positive indicates sink and negative indicates source) will slightly decrease. The carbon sequestration is primarily attributed to elevated atmospheric carbon dioxide and nitrogen deposition. Forests, including both deciduous and evergreen, show the largest increase in carbon storage as compared with other biomes, while cropland carbon storage shows a small decrease. The sequestered carbon will be primarily stored in vegetation for deciduous forest and in soil for evergreen forest. The central and eastern SEUS will sequester more carbon, while the western portion of the SEUS will release carbon to the atmosphere. The combined effects of climate and atmospheric changes on carbon fluxes and storage vary among climate models and climate scenarios. The largest increase in carbon storage would occur under the A1B climate scenario simulated by the NCAR climate model. Generally, the A1B scenario would result in more carbon sequestration than A2 and B1 scenarios; and the projected climate condition by the NCAR model would result in more carbon sequestration than other climate models