My research interests span cryptography, computer security, and privacy with a large focus on blockchain-based systems and distributed protocols. Broadly, I work on interdisciplinary projects that combine knowledge from various fields toward the design of secure and efficient systems and protocols. In my research, I look for real life problems and build solutions backed by rigorous theoretical foundations as well as efficient implementations and thorough performance testing. I also work on conceptual projects that aim to bridge the gap between theory and practice of cryptography.

Current Projects
  • Secure performance boosters for decentralized resource markets: Develop secure sidechain-based frameworks for boosting performance and improving scalability of Web 3.0 applications, in particular the paradigm of blockchain-based resource markets. This is in addition to laying down foundations of modular provable security for these systems. More about our sidechain-based performance booster framework, chainBoost, can be found in our paper.
    Funded by NSF

  • Basing cryptography on biochemical assumptions: Construct bounded-query memory devices, or consumable tokens, from proteins, and use them in various cryptographic applications such as digital lockers and bounded-execution programs. More about these biological consumable tokens, their security and applications, can be found in our (paper, Eurocrypt 2022). Also, for a comparison of the no-cloning principle of unclonable polymers and that of quantum computing, in terms of building unclonable cryptography, check our (paper, Secrypt 2023).

  • Delegation of cryptographic capabilities: Build constructions for delegating cryptographic capabilities, such as digital signatures and zero knowledge proofs, that are timed, revocable and anonymous, and explore their applications to Web 3.0. More about acheiving this notion for proxy signatures can be found in our paper.
    Funded by Protocol Labs

  • State growth control and pricing for DeFi systems: Develop layer-two solutions to reduce the blockchain storage-footprint of DeFi applications (such as automated market makers), in addition to developing multidimensional transaction fee mechanisms under these multi-layer solutions.
    Funded by Uniswap Foundation

  • Privacy and blockchains: Build a privacy-preserving smart contract (PPSC) scheme using fully homomorphic encryption and non-interactive zero knowledge proofs. More about our PPSC framework can be found in our (paper, EuroS&P 2023), and more about current private computing solutions for blockchains can be found in our (SoK paper, EuroS&P 2022).

  • Privacy and machine learning: Use privacy technologies—MPC, FHE, threshold cryptography, and zero knowledge proofs—to tackle privacy issues in machine learning (covering both federated learning and inference), and explore directions to support dynamic participation in these protocols under restricted interaction patterns. More about our anonymity framework for private federated learning can be found in our paper.
    Funded by UConn Research Excellence Award

Previous Projects
  • Gage MPC: Develop a new MPC model to circumvent the leakage of the residual function, and enforce complete fairness, in non-interactive MPC (NIMPC). We introduce new primitives—monetary incentivised time lock capsules and a robust version of garbled circuits—and combine them with smart contracts to economically incentivize participants/miners to evaluate the intended MPC functionality. More about Gage MPC can be found in our paper (Gage MPC, PETS 2021).

  • CacheCash: A system that provides a decentralized content delivery network (CDN) service by forming a distributed bandwidth marketplace powered by a cryptocurrency. This project covered several components including a threat modeling framework for cryptocurrencies (ABC, CryBlock 2019), a defense mechanism against cache accounting attacks (CAPnet, IEEE CNS 2019), a lightweight probabilistic micropayment scheme (MicroCash, FC 2020), and CacheCash (CacheCash, PhD Thesis 2019), the main system that integrates these modules together along with innovative cryptographic and financial security defenses.

  • Privacy-preserving programming compiler extension: Build the first framework to support pointers to private data for privacy preserving programming compilers. This involved extending the PICCO compiler to implement this framework, and studying the efficiency trade-offs of using pointers to private data in secure algorithms and protocols. Read about this framework in our TOPS 2017 paper.

  • Privacy-preserving genome testing: Develop protocols that perform genome tests to detect gene mutations without revealing any information about the underlying genome sequences. This project was a submission to the 4th iDash Privacy & Security Workshop competition. Read about these protocols in our BMC 2015 paper.