Master Thesis
Privacy-Preserving Expense Splitting on Smart Contracts
Expense splitting applications like Splitwise or Tricount have become ubiquitous for managing shared costs among roommates, friends, and travel groups. However, these centralized platforms require users to reveal sensitive financial information to a trusted third party, including group membership, transaction amounts, and payment patterns. This thesis explores the design and implementation of a decentralized expense splitting system that preserves user privacy while operating on a blockchain through smart contracts.
The goal is to enable $n$ users to jointly track and settle shared expenses without relying on any trusted intermediary. Unlike traditional payment splitting apps, the system should hide sensitive details such as who is transacting with whom, the amounts being exchanged, and even the membership of expense-sharing groups. Recent work on Silent Splitter [1] demonstrates privacy-preserving expense splitting using Distributed Point Functions (DPFs) in a two-server setting, but translating these techniques to the smart contract environment presents unique challenges due to the public nature of blockchain execution and limited computational capabilities.
This thesis should investigate cryptographic techniques suitable for on-chain deployment, such as zero-knowledge proofs, homomorphic commitments, or adaptations of DPF-based approaches. The student will design protocols for secure group formation, private transaction recording, and confidential settlement, implement a prototype smart contract system, and evaluate its performance in terms of gas costs, privacy guarantees, and usability compared to centralized alternatives. Additionally, the student should investigate optimization techniques to the underlying transaction graph to minimize the number and cost of settlement transactions while maintaining privacy guarantees.
References
[1] Silent Splitter: Privacy for Payment Splitting via New Protocols for Distributed Point Functions
[2] Zether: Towards Privacy in a Smart Contract World