This paper focuses on how to use consortium blockchain to improve the regulation of peer-to-peer(P2 P) lending market. The partial decentralized consortium blockchain with limited pre-set nodes can well improve transp...This paper focuses on how to use consortium blockchain to improve the regulation of peer-to-peer(P2 P) lending market. The partial decentralized consortium blockchain with limited pre-set nodes can well improve transparency and security, which is suitable for financial regulation. Considering irregularities of the P2P lending market, the Hyperledger-based Peer-to-Peer Lending System(HyperP2PLS) is proposed. First elaborate the application scenario and business logic of the system, where a national P2P Lending Trading Center will be established to integrate all transactions and information of P2P lending market. Then construct the system architecture consisting of the blockchain network, HTTP server, and applications. The algorithm of implementation process and the web application for users have been well illustrated. The performance analysis shows that HyperP2PLS can guarantee the reliability, safety, transparency and efficiency.展开更多
The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent require...The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.展开更多
With the great commercial success of several IPTV (internet protocal television) applications, PPLive has received more and more attention from both industry and academia. At present, PPLive system is one of the most ...With the great commercial success of several IPTV (internet protocal television) applications, PPLive has received more and more attention from both industry and academia. At present, PPLive system is one of the most popular instances of IPTV applications which attract a large number of users across the globe; however, the dramatic rise in popularity makes it more likely to become a vulnerable target. The main contribution of this work is twofold. Firstly, a dedicated distributed crawler system was proposed and its crawling performance was analyzed, which was used to evaluate the impact of pollution attack in P2P live streaming system. The measurement results reveal that the crawler system with distributed architecture could capture PPLive overlay snapshots with more efficient way than previous crawlers. To the best of our knowledge, our study work is the first to employ distributed architecture idea to design crawler system and discuss the crawling performance of capturing accurate overlay snapshots for P2P live streaming system. Secondly, a feasible and effective pollution architecture was proposed to deploy content pollution attack in a real-world P2P live streaming system called PPLive, and deeply evaluate the impact of pollution attack from following five aspects:dynamic evolution of participating users, user lifetime characteristics, user connectivity-performance, dynamic evolution of uploading polluted chunks and dynamic evolution of pollution ratio. Specifically, the experiment results show that a single polluter is capable of compromising all the system and its destructiveness is severe.展开更多
As the COVID-19 pandemic adversely affects the financial markets,a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress.Using the loan book d...As the COVID-19 pandemic adversely affects the financial markets,a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress.Using the loan book database of Mintos(Latvia)and employing logit regression method,we provide evidence of the pandemic-induced exposure to default risk in the marketplace lending market.Our analysis indicates that the probability of default increases from 0.056 in the pre-pandemic period to 0.079 in the post-pandemic period.COVID-19 pandemic has a significant impact on default risk during May and June of 2020.We also find that the magnitude of the impact of COVID-19 risk is higher for borrowers with lower credit ratings and in countries with low levels of FinTech adoption.Our main findings are robust to sample selection bias allowing for a better understanding of and quantifying risks related to FinTech loans during the pandemic and periods of overall economic distress.展开更多
Currently cellular networks do not have sufficient capacity to accommodate the exponential growth of mobile data requirements.Data can be delivered between mobile terminals through peer-to-peer WiFi communications(e.g...Currently cellular networks do not have sufficient capacity to accommodate the exponential growth of mobile data requirements.Data can be delivered between mobile terminals through peer-to-peer WiFi communications(e.g.WiFi direct),but contacts between mobile terminals are frequently disrupted because of the user mobility.In this paper,we propose a Subscribe-and-Send architecture and an opportunistic forwarding protocol for it called HPRO.Under Subscribe-and-Send,a user subscribes contents on the Content Service Provider(CSP) but does not download the subscribed contents.Some users who have these contents deliver them to the subscribers through WiFi opportunistic peer-to-peer communications.Numerical simulations provide a robust evaluation of the forwarding performance and the traffic offloading performance of Subscribe-and-Send and HPRO.展开更多
Peer-to-peer computation offloading has been a promising approach that enables resourcelimited Internet of Things(IoT)devices to offload their computation-intensive tasks to idle peer devices in proximity.Different fr...Peer-to-peer computation offloading has been a promising approach that enables resourcelimited Internet of Things(IoT)devices to offload their computation-intensive tasks to idle peer devices in proximity.Different from dedicated servers,the spare computation resources offered by peer devices are random and intermittent,which affects the offloading performance.The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions.In this work,we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals,dynamic interuser interference,and opportunistic availability of peer devices.Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion,which takes into consideration its energy consumption,task delay,and task loss due to buffer overflow.The dynamic decision process among multiple requestors is formulated as a stochastic game.By constructing the post-decision states,a decentralized online offloading algorithm is proposed,where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations.Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms,especially in the scenarios with large task arrival probability or small helper availability probability.展开更多
Using a large dataset obtained from ＂Paipaidai,＂ an online peer-to-peer lending platform in China, we examine whether credit officers＇ mood affects the efficiency of credit approval from a perspective of individual...Using a large dataset obtained from ＂Paipaidai,＂ an online peer-to-peer lending platform in China, we examine whether credit officers＇ mood affects the efficiency of credit approval from a perspective of individual decision-making. Refering to studies in psychology and financial economics, we employ season, temperature and weather as mood proxies, and control the variables related to the quality of the loan to study credit approval behavior under different mood conditions. The results suggest that the efficiency of credit approval by individual credit officers is significantly correlated with their mood--a positive mood improves efficiency, while a negative mood reduces it. Specifically, loans examined under better mood conditions （e.g., during spring, comfortable temperatures, and sunny days） have a significantly higher probability of approval, but a lower probability of default if approved; and those examined under poorer mood conditions show a lower probability of approval and a higher probability of default if approved. This effect of mood is even stronger when a loan application is more complex, atypical, or unusual to evaluate. Moreover, investor sentiment, denoted by closed-end fund premiums, has the same effect on credit approval as well.展开更多
This study investigates the effect of voluntary disclosures on lending decisions in the repeated game.Using a unique dataset from a peer-to-peer lending platform,"ppdai" (poipaidai),we document that voluntar...This study investigates the effect of voluntary disclosures on lending decisions in the repeated game.Using a unique dataset from a peer-to-peer lending platform,"ppdai" (poipaidai),we document that voluntary disclosures in the repeated game play a stronger role in promoting funding success than those in the one-shot game.We argue that voluntary disclosures improve the bidding activity in the repeated game through which they increase funding success.In addition,the greater impact of voluntary disclosures on funding success in the repeated game only holds for loans without a personal guarantee attribution.Our extended results suggest that the subjective voluntary disclosures in the repeated game have greater information content only when borrowers have a successful borrowing experience.We also point out that voluntary disclosures in the repeated game are associated with a lower probability of default.Our results are robust to the Heckman two-step estimation that addresses the self-selection effect and a specification designed to rule out the alternative explanation from reputation in the repeated game.Our study provides new insights into the real effects of costless,voluntary and unverifiable disclosures on lending decisions.展开更多
A dramatic surge in online peer-to-peer(P2P)lending emerged in China,where(under conditions of credit deficiency)it took only three years for the size of the P2P lending market in China to reach four times that of the...A dramatic surge in online peer-to-peer(P2P)lending emerged in China,where(under conditions of credit deficiency)it took only three years for the size of the P2P lending market in China to reach four times that of the United States and ten times that of the United Kingdom.The literature indicates that ownership structure is an important factor that influences P2P lending firms’performance,while research on the underlying mechanisms remain insufficient.This study analyzes the data of P2P lending companies between June 2016 and March 2017.The results demonstrate that although ownership structure has minimal direct effect on the turnover volume and number of lenders and borrowers,it moderates the effects of firm age,interest rate,and loan term on firm performance.These results enrich the property theory and shed light on how P2P lending firms with different ownership structures could succeed when there is institutional deficiency.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.71872020 and No.71402008the Corporate Finance and Innovation Development Research Center in BUPT
文摘This paper focuses on how to use consortium blockchain to improve the regulation of peer-to-peer(P2 P) lending market. The partial decentralized consortium blockchain with limited pre-set nodes can well improve transparency and security, which is suitable for financial regulation. Considering irregularities of the P2P lending market, the Hyperledger-based Peer-to-Peer Lending System(HyperP2PLS) is proposed. First elaborate the application scenario and business logic of the system, where a national P2P Lending Trading Center will be established to integrate all transactions and information of P2P lending market. Then construct the system architecture consisting of the blockchain network, HTTP server, and applications. The algorithm of implementation process and the web application for users have been well illustrated. The performance analysis shows that HyperP2PLS can guarantee the reliability, safety, transparency and efficiency.
基金supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.GPIP:2040-611-2024。
文摘The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.
基金Project(2007CB311106) supported by National Basic Research Program of ChinaProject(242-2009A82) supported by National Information Security Special Plan Program of China
文摘With the great commercial success of several IPTV (internet protocal television) applications, PPLive has received more and more attention from both industry and academia. At present, PPLive system is one of the most popular instances of IPTV applications which attract a large number of users across the globe; however, the dramatic rise in popularity makes it more likely to become a vulnerable target. The main contribution of this work is twofold. Firstly, a dedicated distributed crawler system was proposed and its crawling performance was analyzed, which was used to evaluate the impact of pollution attack in P2P live streaming system. The measurement results reveal that the crawler system with distributed architecture could capture PPLive overlay snapshots with more efficient way than previous crawlers. To the best of our knowledge, our study work is the first to employ distributed architecture idea to design crawler system and discuss the crawling performance of capturing accurate overlay snapshots for P2P live streaming system. Secondly, a feasible and effective pollution architecture was proposed to deploy content pollution attack in a real-world P2P live streaming system called PPLive, and deeply evaluate the impact of pollution attack from following five aspects:dynamic evolution of participating users, user lifetime characteristics, user connectivity-performance, dynamic evolution of uploading polluted chunks and dynamic evolution of pollution ratio. Specifically, the experiment results show that a single polluter is capable of compromising all the system and its destructiveness is severe.
文摘As the COVID-19 pandemic adversely affects the financial markets,a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress.Using the loan book database of Mintos(Latvia)and employing logit regression method,we provide evidence of the pandemic-induced exposure to default risk in the marketplace lending market.Our analysis indicates that the probability of default increases from 0.056 in the pre-pandemic period to 0.079 in the post-pandemic period.COVID-19 pandemic has a significant impact on default risk during May and June of 2020.We also find that the magnitude of the impact of COVID-19 risk is higher for borrowers with lower credit ratings and in countries with low levels of FinTech adoption.Our main findings are robust to sample selection bias allowing for a better understanding of and quantifying risks related to FinTech loans during the pandemic and periods of overall economic distress.
基金supported by the National Natural Science Foundation of China under Grants No. 61100208,No. 61100205the Natural Science Foundation of Jiangsu Province under Grant No. BK2011169+1 种基金the Foundation of Beijing University of Posts and Telecommunications under Grant No. 2013RC0309supported by the EU FP7 Project REC-OGNITION:Relevance and Cognition for SelfAwareness in a Content-Centric Internet
文摘Currently cellular networks do not have sufficient capacity to accommodate the exponential growth of mobile data requirements.Data can be delivered between mobile terminals through peer-to-peer WiFi communications(e.g.WiFi direct),but contacts between mobile terminals are frequently disrupted because of the user mobility.In this paper,we propose a Subscribe-and-Send architecture and an opportunistic forwarding protocol for it called HPRO.Under Subscribe-and-Send,a user subscribes contents on the Content Service Provider(CSP) but does not download the subscribed contents.Some users who have these contents deliver them to the subscribers through WiFi opportunistic peer-to-peer communications.Numerical simulations provide a robust evaluation of the forwarding performance and the traffic offloading performance of Subscribe-and-Send and HPRO.
基金supported by National Natural Science Foundation of China (No. 62101601)
文摘Peer-to-peer computation offloading has been a promising approach that enables resourcelimited Internet of Things(IoT)devices to offload their computation-intensive tasks to idle peer devices in proximity.Different from dedicated servers,the spare computation resources offered by peer devices are random and intermittent,which affects the offloading performance.The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions.In this work,we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals,dynamic interuser interference,and opportunistic availability of peer devices.Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion,which takes into consideration its energy consumption,task delay,and task loss due to buffer overflow.The dynamic decision process among multiple requestors is formulated as a stochastic game.By constructing the post-decision states,a decentralized online offloading algorithm is proposed,where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations.Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms,especially in the scenarios with large task arrival probability or small helper availability probability.
文摘Using a large dataset obtained from ＂Paipaidai,＂ an online peer-to-peer lending platform in China, we examine whether credit officers＇ mood affects the efficiency of credit approval from a perspective of individual decision-making. Refering to studies in psychology and financial economics, we employ season, temperature and weather as mood proxies, and control the variables related to the quality of the loan to study credit approval behavior under different mood conditions. The results suggest that the efficiency of credit approval by individual credit officers is significantly correlated with their mood--a positive mood improves efficiency, while a negative mood reduces it. Specifically, loans examined under better mood conditions （e.g., during spring, comfortable temperatures, and sunny days） have a significantly higher probability of approval, but a lower probability of default if approved; and those examined under poorer mood conditions show a lower probability of approval and a higher probability of default if approved. This effect of mood is even stronger when a loan application is more complex, atypical, or unusual to evaluate. Moreover, investor sentiment, denoted by closed-end fund premiums, has the same effect on credit approval as well.
基金This study is supported by National Natural Science Foundation of China(No.71272150)National Social Science Foundation of China(No.15AZD012)Social Science Research Fund of Inner Mongolia Autonom ous Region in China(2017NDC145).
文摘This study investigates the effect of voluntary disclosures on lending decisions in the repeated game.Using a unique dataset from a peer-to-peer lending platform,"ppdai" (poipaidai),we document that voluntary disclosures in the repeated game play a stronger role in promoting funding success than those in the one-shot game.We argue that voluntary disclosures improve the bidding activity in the repeated game through which they increase funding success.In addition,the greater impact of voluntary disclosures on funding success in the repeated game only holds for loans without a personal guarantee attribution.Our extended results suggest that the subjective voluntary disclosures in the repeated game have greater information content only when borrowers have a successful borrowing experience.We also point out that voluntary disclosures in the repeated game are associated with a lower probability of default.Our results are robust to the Heckman two-step estimation that addresses the self-selection effect and a specification designed to rule out the alternative explanation from reputation in the repeated game.Our study provides new insights into the real effects of costless,voluntary and unverifiable disclosures on lending decisions.
文摘A dramatic surge in online peer-to-peer(P2P)lending emerged in China,where(under conditions of credit deficiency)it took only three years for the size of the P2P lending market in China to reach four times that of the United States and ten times that of the United Kingdom.The literature indicates that ownership structure is an important factor that influences P2P lending firms’performance,while research on the underlying mechanisms remain insufficient.This study analyzes the data of P2P lending companies between June 2016 and March 2017.The results demonstrate that although ownership structure has minimal direct effect on the turnover volume and number of lenders and borrowers,it moderates the effects of firm age,interest rate,and loan term on firm performance.These results enrich the property theory and shed light on how P2P lending firms with different ownership structures could succeed when there is institutional deficiency.
