I am a first year PhD student in the College of Computing at Georgia Tech, where I am advised by Prof. Vassilis Zikas. My research primarily focuses on privacy-preserving machine learning and applied cryptography. I am currently working on validating claimed differential privacy guarantees of a mechanism in a black-box way to ensure fair auditing.
Prior to starting my PhD, I was a software engineer at JPMorgan Chase and Co. where I was contributing to research projects with the J.P. Morgan AI Research team, particulary the AlgoCRYPT COE under the mentorship of Dr. Antigoni Polychroniadou. I graduated with a Bachelors of Technology (B.Tech) in Computer Science from Vellore Institute of Technology, India in July 2023.
I am broadly interested in enhancing privacy and security in real-world AI systems. I aim to integrate privacy-enhancing technologies (PETs) in distributed systems to protect sensitive data, while maintaining practical utility and accuracy.
As large language models (LLMs) become more powerful, the computation required to run these models is increasingly outsourced to a third-party cloud. While this saves clients’ computation, it risks leaking the clients’ LLM queries to the cloud provider. Fully homomorphic encryption (FHE) presents a natural solution to this problem: simply encrypt the query and evaluate the LLM homomorphically on the cloud machine. The result remains encrypted and can only be learned by the client who holds the secret key. In this work, we present a GPU-accelerated implementation of FHE and use this implementation to benchmark an encrypted GPT-2 forward pass, with runtimes over 200x faster than the CPU baseline. We also present novel and extensive experimental analysis of approximations of LLM activation functions to maintain accuracy while achieving this performance.
@inproceedings{castro2025encryptedllm,title={Encrypted{LLM}: Privacy-Preserving Large Language Model Inference via {GPU}-Accelerated Fully Homomorphic Encryption},author={de Castro, Leo and Escudero, Daniel and Agrawal, Adya and Polychroniadou, Antigoni and Veloso, Manuela},booktitle={Forty-second International Conference on Machine Learning},year={2025},url={https://openreview.net/forum?id=PGNff6H1TV},}
Public exchanges like the New York Stock Exchange and NASDAQ act as auctioneers in a public double auction system, where buyers submit their highest bids and sellers offer their lowest asking prices, along with the number of shares (volume) they wish to trade. The auctioneer matches compatible orders and executes the trades when a match is found. However, auctioneers involved in high-volume exchanges, such as dark pools, may not always be reliable. They could exploit their position by engaging in practices like front-running or face significant conflicts of interest—ethical breaches that have frequently resulted in hefty fines and regulatory scrutiny within the financial industry. Previous solutions, based on the use of fully homomorphic encryption (Asharov et al., AAMAS 2020), encrypt orders ensuring that information is revealed only when a match occurs. However, this approach introduces significant computational overhead, making it impractical for high-frequency trading environments such as dark pools. In this work, we propose a new system based on differential privacy combined with lightweight encryption, offering an efficient and practical solution that mitigates the risks of an untrustworthy auctioneer. Specifically, we introduce a new concept called Indifferential Privacy, which can be of independent interest, where a user is indifferent to whether certain information is revealed after some special event, unlike standard differential privacy. For example, in an auction, it’s reasonable to disclose the true volume of a trade once all of it has been matched. Moreover, our new concept of Indifferential Privacy allows for maximum matching, which is impossible with conventional differential privacy.
@inproceedings{anonymous2024indifferential,title={Indifferential Privacy: A New Paradigm and Its Applications to Optimal Matching in Dark Pool Auctions},author={Polychroniadou, Antigoni and Chan, T-H. Hubert and Agrawal, Adya},booktitle={The 24th International Conference on Autonomous Agents and Multi-Agent Systems},year={2025},url={https://openreview.net/forum?id=YGKL8fKjbd},}
