---
res:
  bibo_abstract:
  - "Payment channel networks are a promising approach to improve the scalability
    bottleneck\r\nof cryptocurrencies. Two design principles behind payment channel
    networks are\r\nefficiency and privacy. Payment channel networks improve efficiency
    by allowing users\r\nto transact in a peer-to-peer fashion along multi-hop routes
    in the network, avoiding\r\nthe lengthy process of consensus on the blockchain.
    Transacting over payment channel\r\nnetworks also improves privacy as these transactions
    are not broadcast to the blockchain.\r\nDespite the influx of recent protocols
    built on top of payment channel networks and\r\ntheir analysis, a common shortcoming
    of many of these protocols is that they typically\r\nfocus only on either improving
    efficiency or privacy, but not both. Another limitation\r\non the efficiency front
    is that the models used to model actions, costs and utilities of\r\nusers are
    limited or come with unrealistic assumptions.\r\nThis thesis aims to address some
    of the shortcomings of recent protocols and algorithms\r\non payment channel networks,
    particularly in their privacy and efficiency aspects. We\r\nfirst present a payment
    route discovery protocol based on hub labelling and private\r\ninformation retrieval
    that hides the route query and is also efficient. We then present\r\na rebalancing
    protocol that formulates the rebalancing problem as a linear program\r\nand solves
    the linear program using multiparty computation so as to hide the channel\r\nbalances.
    The rebalancing solution as output by our protocol is also globally optimal.\r\nWe
    go on to develop more realistic models of the action space, costs, and utilities
    of\r\nboth existing and new users that want to join the network. In each of these
    settings,\r\nwe also develop algorithms to optimise the utility of these users
    with good guarantees\r\non the approximation and competitive ratios.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Michelle X
      foaf_name: Yeo, Michelle X
      foaf_surname: Yeo
      foaf_workInfoHomepage: http://www.librecat.org/personId=2D82B818-F248-11E8-B48F-1D18A9856A87
    orcid: 0009-0001-3676-4809
  bibo_doi: 10.15479/14506
  dct_date: 2023^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2663-337X
  dct_language: eng
  dct_publisher: Institute of Science and Technology Austria@
  dct_title: Advances in efficiency and privacy in payment channel network analysis@
...