Distributed credit networks, such as Ripple [18] and Stellar [21], are
becoming popular as an alternative means for financial transactions.
However, the current designs do not preserve user privacy or are
not truly decentralized. In this paper, we explore the creation of
a distributed credit network that preserves user and transaction
privacy and unlinkability. We propose BlAnC, a novel, fully decentralized
blockchain-based credit network where credit transfer
between a sender-receiver pair happens on demand. In BlAnC, multiple
concurrent transactions can occur seamlessly, and malicious
network actors that do not follow the protocols and/or disrupt
operations can be identified efficiently.
We perform security analysis of our proposed protocols in the
universal composability framework to demonstrate its strength,
and discuss how our network handles operational dynamics. We
also present preliminary experiments and scalability analyses.
more »
« less
Balance Transfers and Bailouts in Credit Networks using Blockchains
In this paper, we propose a technique for rebalancing link weights in decentralized credit networks. Credit networks are peer-to-peer trust-based networks that enable fast and inexpensive cross-currency transactions compared to traditional bank wire transfers. Although researchers have studied security of transactions and privacy of users of such networks, and have invested significant efforts into designing efficient routing algorithms for credit networks, comparatively little work has been done in the area of replenishing credit links of users in the network. This is achieved by a process called rebalancing that enables a poorly funded user to create incoming as well as outgoing credit links. We propose a system where a user with zero or no link weights can create incoming links with existing, trusted users in the network, in a procedure we call balance transfer, followed by creating outgoing links to existing or new users that would like to join the network, a process we call bailout. Both these processes together constitute our proposed rebalancing mechanism.
more »
« less
- PAR ID:
- 10208830
- Date Published:
- Journal Name:
- 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)
- Page Range / eLocation ID:
- 1 to 3
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e.g., positive vs. negative. Since negative links may have different properties from the positive ones and can also significantly affect the quality of network embedding. Thus in this paper, we propose a novel network embedding framework SNEA to learn Signed Network Embedding via graph Attention. In particular, we propose a masked self-attentional layer, which leverages self-attention mechanism to estimate the importance coefficient for pair of nodes connected by different type of links during the embedding aggregation process. Then SNEA utilizes the masked self-attentional layers to aggregate more important information from neighboring nodes to generate the node embeddings based on balance theory. Experimental results demonstrate the effectiveness of the proposed framework through signed link prediction task on several real-world signed network datasets.more » « less
-
With the prevalence of interaction on social media, data compiled from these networks are perfect for analyzing social trends. One such trend that this paper aims to address is political homophily. Evidence of political homophily is well researched and indicates that people have a strong tendency to interact with others with similar political ideologies. Additionally, as links naturally form in a social network, either through recommendations or indirect interaction, new links are very likely to reinforce communities. This serves to make social media more insulated and ultimately more polarizing. We aim to address this problem by providing link recommendations that will reduce network homophily. We propose several variants of common neighbor-based link prediction algorithms that aim to recommend links to users who are similar but also would decrease homophily. We demonstrate that acceptance of these recommendations can indeed reduce the homophily of the network, whereas acceptance of link recommendations from a standard common neighbors algorithm does not.more » « less
-
Hemmer, Philip R. ; Migdall, Alan L. (Ed.)We study a quantum switch that creates shared end-to-end entangled quantum states to multiple sets of users that are connected to it. Each user is connected to the switch via an optical link across which bipartite Bell-state entangled states are generated in each time-slot with certain probabilities, and the switch merges entanglements of links to create end-to-end entanglements for users. One qubit of an entanglement of a link is stored at the switch and the other qubit of the entanglement is stored at the user corresponding to the link. Assuming that qubits of entanglements of links decipher after one time-slot, we characterize the capacity region, which is defined as the set of arrival rates of requests for end-to-end entanglements for which there exists a scheduling policy that stabilizes the switch. We propose a Max-Weight scheduling policy and show that it stabilizes the switch for all arrival rates that lie in the capacity region. We also provide numerical results to support our analysis.more » « less
-
null (Ed.)In many application settings involving networks, such as messages between users of an on-line social network or transactions between traders in financial markets, the observed data consist of timestamped relational events, which form a continuous-time network. We propose the Community Hawkes Independent Pairs (CHIP) generative model for such networks. We show that applying spectral clustering to an aggregated adjacency matrix constructed from the CHIP model provides consistent community detection for a growing number of nodes and time duration. We also develop consistent and computationally efficient estimators for the model parameters. We demonstrate that our proposed CHIP model and estimation procedure scales to large networks with tens of thousands of nodes and provides superior fits than existing continuous-time network models on several real networks.more » « less