Rising Stars 2020:

Madhumitha Harishankar

PhD Candidate

Carnegie Mellon University

Areas of Interest

  • Operating Systems and Networking
  • Wireless Networking
  • Blockchain
  • Network Economics


PayPlace: Secure and Flexible Operator-Mediated Payments in Blockchain Marketplaces at Scale


Decentralized marketplace applications demand fast, cheap and easy-to-use cryptocurrency payment mechanisms to facilitate high transaction volumes. The standard solution for off-chain payments, state channels, are optimized for frequent transactions between two entities and impose prohibitive liquidity and capital requirements on payment senders for marketplace transactions. We propose PayPlace, a scalable off-chain protocol for payments between consumers and sellers. Using PayPlace, consumers establish a virtual unidirectional payment channel with an intermediary operator to pay for their transactions. Unlike state channels, however, the PayPlace operator can reference the custodial funds accrued off-chain in these channels to in-turn make tamper-proof off-chain payments to merchants, without locking up corresponding capital in channels with merchants. Our design ensures that new payments made to merchants are guaranteed to be safe once notarized and provably mitigates well-known drawbacks in previous constructions like the data availability attack and ensures that neither consumers nor merchants need to be online to ensure continued safety of their notarized funds. We show that the on-chain monetary and computational costs for PayPlace is O(1) in the number of payment transactions processed, and is near-constant in other parameters in most scenarios. PayPlace can hence scale the payment throughput for large-scale marketplaces at no marginal cost and is orders of magnitude cheaper than the state-of-art solution for non-pairwise off-chain payments, Zero Knowledge Rollups.


I am a 5th year PhD Candidate at the ECE dept. of Carnegie Mellon University, jointly advised by Prof. Carlee Joe-Wong and Prof. Patrick Tague. My research primarily revolves around the theory and practice of incentive-compatibility and trust in user-centric resource allocation in wireless networks. My areas of interest include blockchains, network economics, reinforcement learning and mechanism design. I was the named as a Dean's Fellow by the Carnegie Institute of Technology and received an Honorable Mention from the NSF GRFP in 2017. Prior to starting my doctorate studies, I was a software engineering for about three years at AWS and Barclays Investment Bank. I graduated with a B.S. in ECE from Rutgers University in 2013.

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