arXiv subjects

Yongzheng Jia

Publications and source records attributed to Yongzheng Jia.

6 recordsLinked to original sources

arXiv2023

Understanding NFT Price Moves through Tweets Keywords Analysis

Non-Fungible Token (NFT) is evolving with the rise of the cryptocurrency market and the development of blockchain techniques, which leads to an emerging NFT market that has become prosperous rapidly then followed by a cooldown. Nevertheless, the overall rise procedure of the NFT market has not been well understood. To this end, we consider that social media communities evolving alongside the market growth, are worth exploring and reasoning about, as the mineable information might unveil the market behaviors. We explore the procedure from the perspective of NFT Twitter communities and its impact on the NFT price moves with two experiments. We perform a Granger causality test on the number of tweets and the NFT price time series and find that the number of tweets has a positive impact on (Granger-causes) the price or reversely for larger part of the 19 top authentic projects but seldom copycat projects. Besides, to investigate the price moves predictability, we experiment on predicting Markov normalized NFT price (representing the direction and magnitude of price moves) given tweets-extracted word features and interpret the feature importance to find insights. Our results show that social media words as the predictors result in all 19 top projects having a testing accuracy evidently above the baseline. Based on the feature importance analysis, we find that both general market-related words and NFT event-related words have a markedly positive contribution in predicting price moves. We summarize the characteristics including categorization and sentiment for the words with the top and least feature importance.

cs.SI↗

arXiv2023

FIAT: Fine-grained Information Audit for Trustless Transborder Data Flow

Auditing the information leakage of latent sensitive features during the transborder data flow has attracted sufficient attention from global digital regulators. However, there is missing a technical approach for the audit practice due to two technical challenges. Firstly, there is a lack of theory and tools for measuring the information of sensitive latent features in a dataset. Secondly, the transborder data flow involves multi-stakeholders with diverse interests, which means the audit must be trustless. Despite the tremendous efforts in protecting data privacy, an important issue that has long been neglected is that the transmitted data in data flows can leak other regulated information that is not explicitly contained in the data, leading to unaware information leakage risks. To unveil such risks trustfully before the actual data transfer, we propose FIAT, a Fine-grained Information Audit system for Trustless transborder data flow. In FIAT, we use a learning approach to quantify the amount of information leakage, while the technologies of zero-knowledge proof and smart contracts are applied to provide trustworthy and privacy-preserving auditing results. Experiments show that large information leakage can boost the predictability of uninvolved information using simple machine-learning models, revealing the importance of information auditing. Further performance benchmarking also validates the efficiency and scalability of the FIAT auditing system.

cs.IT↗

arXiv2022

zkBridge: Trustless Cross-chain Bridges Made Practical

Blockchains have seen growing traction with cryptocurrencies reaching a market cap of over 1 trillion dollars, major institution investors taking interests, and global impacts on governments, businesses, and individuals. Also growing significantly is the heterogeneity of the ecosystem where a variety of blockchains co-exist. Cross-chain bridge is a necessary building block in this multi-chain ecosystem. Existing solutions, however, either suffer from performance issues or rely on trust assumptions of committees that significantly lower the security. Recurring attacks against bridges have cost users more than 1.5 billion USD. In this paper, we introduce zkBridge, an efficient cross-chain bridge that guarantees strong security without external trust assumptions. With succinct proofs, zkBridge not only guarantees correctness, but also significantly reduces on-chain verification cost. We propose novel succinct proof protocols that are orders-of-magnitude faster than existing solutions for workload in zkBridge. With a modular design, zkBridge enables a broad spectrum of use cases and capabilities, including message passing, token transferring, and other computational logic operating on state changes from different chains. To demonstrate the practicality of zkBridge, we implemented a prototype bridge from Cosmos to Ethereum, a particularly challenging direction that involves large proof circuits that existing systems cannot efficiently handle. Our evaluation shows that zkBridge achieves practical performance: proof generation takes less than 20 seconds, while verifying proofs on-chain costs less than 230K gas. For completeness, we also implemented and evaluated the direction from Ethereum to other EVM-compatible chains (such as BSC) which involves smaller circuits and incurs much less overhead.

cs.CR↗

arXiv2017

Towards Economic Models for MOOC Pricing Strategy Design

MOOCs have brought unprecedented opportunities of making high-quality courses accessible to everybody. However, from the business point of view, MOOCs are often challenged for lacking of sustainable business models, and academic research for marketing strategies of MOOCs is also a blind spot currently. In this work, we try to formulate the business models and pricing strategies in a structured and scientific way. Based on both theoretical research and real marketing data analysis from a MOOC platform, we present the insights of the pricing strategies for existing MOOC markets. We focus on the pricing strategies for verified certificates in the B2C markets, and also give ideas of modeling the course sub-licensing services in B2B markets.

cs.CY↗

arXiv2016

An Optimization Framework For Online Ride-sharing Markets

Taxi services and product delivery services are instrumental for our modern society. Thanks to the emergence of sharing economy, ride-sharing services such as Uber, Didi, Lyft and Google's Waze Rider are becoming more ubiquitous and grow into an integral part of our everyday lives. However, the efficiency of these services are severely limited by the sub-optimal and imbalanced matching between the supply and demand. We need a generalized framework and corresponding efficient algorithms to address the efficient matching, and hence optimize the performance of these markets. Existing studies for taxi and delivery services are only applicable in scenarios of the one-sided market. In contrast, this work investigates a highly generalized model for the taxi and delivery services in the market economy (abbreviated as"taxi and delivery market") that can be widely used in two-sided markets. Further, we present efficient online and offline algorithms for different applications. We verify our algorithm with theoretical analysis and trace-driven simulations under realistic settings.

cs.SI↗

arXiv2016

Online Scaling of NFV Service Chains across Geo-distributed Datacenters

Network Function Virtualization (NFV) is an emerging paradigm that turns hardware-dependent implementation of network functions (i.e., middleboxes) into software modules running on virtualized platforms, for significant cost reduction and ease of management. Such virtual network functions (VNFs) commonly constitute service chains, to provide network services that traffic flows need to go through. Efficient deployment of VNFs for network service provisioning is key to realize the NFV goals. Existing efforts on VNF placement mostly deal with offline or one-time placement, ignoring the fundamental, dynamic deployment and scaling need of VNFs to handle practical time-varying traffic volumes. This work investigates dynamic placement of VNF service chains across geo-distributed datacenters to serve flows between dispersed source and destination pairs, for operational cost minimization of the service chain provider over the entire system span. An efficient online algorithm is proposed, which consists of two main components: (1) A regularization-based approach from online learning literature to convert the offline optimal deployment problem into a sequence of one-shot regularized problems, each to be efficiently solved in one time slot; (2) An online dependent rounding scheme to derive feasible integer solutions from the optimal fractional solutions of the one-shot problems, and to guarantee a good competitive ratio of the online algorithm over the entire time span. We verify our online algorithm with solid theoretical analysis and trace-driven simulations under realistic settings.

cs.NI↗