Cryptocurrency in the Aftermath: Unveiling the Impact of the SVB Collapse

In this paper, we explore the aftermath of the Silicon Valley Bank (SVB) collapse, with a particular focus on its impact on crypto markets. We conduct a multi-dimensional investigation, which includes a factual summary, analysis of user sentiment, and examination of market performance. Based on such efforts, we uncover a somewhat counterintuitive finding: the SVB collapse did not lead to the destruction of cryptocurrencies; instead, they displayed resilience

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

Split Unlearning

Split learning is emerging as a powerful approach to decentralized machine learning, but the urgent task of unlearning to address privacy issues presents significant challenges. Conventional methods of retraining from scratch or gradient ascending require all clients' involvement, incurring high computational and communication overhead, particularly in public networks where clients lack resources and may be reluctant to participate in unlearning processes they have no interest. In this short article, we propose \textsc{SplitWiper}, a new framework that integrates the concept of SISA to reduce retraining costs and ensures no interference between the unlearning client and others in public networks. Recognizing the inherent sharding in split learning, we first establish the SISA-based design of \textsc{SplitWiper}. This forms the premise for conceptualizing two unlearning strategies for label-sharing and non-label-sharing scenarios. This article represents an earlier edition, with extensive experiments being conducted for the forthcoming full version.Comment: An earlier edition, with extensive experiments being conducted for the forthcoming full versio

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

Leveraging Architectural Approaches in Web3 Applications -- A DAO Perspective Focused

Architectural design contexts contain a set of factors that influence software application development. Among them, \textit{\textbf{organizational}} design contexts consist of high-level company concerns and how it is structured, for example, stakeholders and development schedule, heavily impacting design considerations. Decentralized Autonomous Organization (DAO), as a vital concept in the Web3 space, is an organization constructed by automatically executed rules such as via smart contracts, holding features of the permissionless committee, transparent proposals, and fair contribution by stakeholders. In this work, we conduct a systematic literature review to summarize how DAO is structured as well as explore its benefits\&challenges in Web3 applications

The Privacy Pillar -- A Conceptual Framework for Foundation Model-based Systems

AI and its relevant technologies, including machine learning, deep learning, chatbots, virtual assistants, and others, are currently undergoing a profound transformation of development and organizational processes within companies. Foundation models present both significant challenges and incredible opportunities. In this context, ensuring the quality attributes of foundation model-based systems is of paramount importance, and with a particular focus on the challenging issue of privacy due to the sensitive nature of the data and information involved. However, there is currently a lack of consensus regarding the comprehensive scope of both technical and non-technical issues that the privacy evaluation process should encompass. Additionally, there is uncertainty about which existing methods are best suited to effectively address these privacy concerns. In response to this challenge, this paper introduces a novel conceptual framework that integrates various responsible AI patterns from multiple perspectives, with the specific aim of safeguarding privacy.Comment: 10 page