Georgia Avarikioti

Dr.sc.ETH

Georgia Avarikioti

Zeta Avarikioti joined Tu Wien as a postdoctoral fellow (FWF ESPRIT program) in 2022. Prior to that, she was a postdoc fellow at IST Austria and a visiting postdoc at Columbia Univeristy. She obtained a PhD in Information Technology and Electrical Engineering at ETH Zurich in 2021.

After her PhD, Zeta Avarikioti was awarded three postdoc fellowships (SNSF Early Postdoc.Mobility, IST Postdoc Fellowship, FWF ESPRIT Program). She has chaired two workshops and participated in the PC of more than 10 conferences.

Her research interests lie in the area of blockchains and specifically on the intersection of distributed computing and game theory. She has mainly focused her research so far on the scalability of blockchains and the game-theoretic analysis of blockchain protocols.

Roles
  • PostDoc Researcher
Contact
Projects (at TU Wien)
Publications (at TU Wien)
    2022
    • Hide & Seek: Privacy-Preserving Rebalancing on Payment Channel Networks
      Avarikioti, G., Pietrzak, K., Salem, I., Schmid, S., Tiwari, S., & Yeo, M. (2022). Hide & Seek: Privacy-Preserving Rebalancing on Payment Channel Networks. In I. Eyal & J. Garay (Eds.), Financial Cryptography and Data Security (pp. 358–373). Springer-Verlag.
      DOI: 10.1007/978-3-031-18283-9_17 Metadata
      Abstract: Payment channels effectively move the transaction load off-chain thereby successfully addressing the inherent scalability problem most cryptocurrencies face. A major drawback of payment channels is the need to “top up” funds on-chain when a channel is depleted. Rebalancing was proposed to alleviate this issue, where parties with depleting channels move their funds along a cycle to replenish their channels off-chain. Protocols for rebalancing so far either introduce local solutions or compromise privacy. In this work, we present an opt-in rebalancing protocol that is both private and globally optimal, meaning our protocol maximizes the total amount of rebalanced funds. We study rebalancing from the framework of linear programming. To obtain full privacy guarantees, we leverage multi-party computation in solving the linear program, which is executed by selected participants to maintain efficiency. Finally, we efficiently decompose the rebalancing solution into incentive-compatible cycles which conserve user balances when executed atomically.