83 citations · 105 across the 25 of their papers we have counts for
25 papers
Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification
Carter Luck, Olive Franzese-McLaughlin, Elisaweta Masserova +3
Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing their internals or training da…
Asymptotic Optimality of the High-Dimensional Gaussian Mechanism and Improved Low-Dimensional Mechanisms for Differential Privacy
Yu Wei, Alexander Bienstock, Antigoni Polychroniadou
The additive noise mechanism is a foundational tool for differential privacy (DP) of -dimensional real-valued vector queries. The Gaussian mechanism, utilizing Gaussian noise, i…
Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
Yvonne Zhou, Mingyu Liang, Ivan Brugere +5
We present the first theoretical convergence analysis of machine learning training under fully homomorphic encryption (FHE), combined with a differentially private (DP) training al…
Scalable Secure Biometric Authentication without Auxiliary Identifiers
Alexander Bienstock, Daniel Escudero, Antigoni Polychroniadou +5
The prevalence of biometric authentication has been on the rise due to its ease of use and elimination of weak passwords. To date, most biometric authentication systems have been d…
TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)
Harish Karthikeyan, Antigoni Polychroniadou
Privacy-preserving aggregation is a cornerstone for AI systems that learn from distributed data without exposing individual records, especially in federated learning and telemetry.…
ZKBoost: Zero-Knowledge Verifiable Training for XGBoost
Nikolas Melissaris, Antigoni Polychroniadou, Akira Takahashi +2
Gradient boosted decision trees, particularly XGBoost, are among the most effective methods for tabular data. As deployment in sensitive settings increases, cryptographic guarantee…
AgentCrypt: Advancing Privacy and (Secure) Computation in AI Agent Collaboration
Harish Karthikeyan, Yue Guo, Leo de Castro +5
As AI agents increasingly operate in complex environments, ensuring reliable, context-aware privacy is critical for regulatory compliance. Traditional access controls are insuffici…
Armadillo: Robust Single-Server Secure Aggregation for Federated Learning with Input Validation
Yiping Ma, Yue Guo, Harish Karthikeyan +1
This paper presents a secure aggregation system Armadillo that has disruptive resistance against adversarial clients, such that any coalition of malicious clients (within the toler…
Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting
Eftychia Makri, Georgios Palaiokrassas, Sarah Bouraga +2
Cryptocurrencies have transformed financial markets with their innovative blockchain technology and volatile price movements, presenting both challenges and opportunities for predi…
Indifferential Privacy: A New Paradigm and Its Applications to Optimal Matching in Dark Pool Auctions
Antigoni Polychroniadou, T. -H. Hubert Chan, Adya Agrawal
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 lo…
MAFE: Enabling Equitable Algorithm Design in Multi-Agent Multi-Stage Decision-Making Systems
Zachary McBride Lazri, Anirudh Nakra, Ivan Brugere +5
Algorithmic fairness is often studied in static or single-agent settings, yet many real-world decision-making systems involve multiple interacting entities whose multi-stage action…
: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning
Harish Karthikeyan, Antigoni Polychroniadou
Our work aims to minimize interaction in secure computation due to the high cost and challenges associated with communication rounds, particularly in scenarios with many clients. I…
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing
Alexander Bienstock, Ujjwal Kumar, Antigoni Polychroniadou
Federated Learning (FL) solutions with central Differential Privacy (DP) have seen large improvements in their utility in recent years arising from the matrix mechanism, while FL s…
Atlas-X Equity Financing: Unlocking New Methods to Securely Obfuscate Axe Inventory Data Based on Differential Privacy
Antigoni Polychroniadou, Gabriele Cipriani, Richard Hua +1
Banks publish daily a list of available securities/assets (axe list) to selected clients to help them effectively locate Long (buy) or Short (sell) trades at reduced financing rate…
Balancing Fairness and Accuracy in Data-Restricted Binary Classification
Zachary McBride Lazri, Danial Dervovic, Antigoni Polychroniadou +3
Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier. For example, in some applications, a cla…
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
Yvonne Zhou, Mingyu Liang, Ivan Brugere +4
The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset. To pre…
Prime Match: A Privacy-Preserving Inventory Matching System
Antigoni Polychroniadou, Gilad Asharov, Benjamin Diamond +6
Inventory matching is a standard mechanism/auction for trading financial stocks by which buyers and sellers can be paired. In the financial world, banks often undertake the task of…
A Canonical Data Transformation for Achieving Inter- and Within-group Fairness
Zachary McBride Lazri, Ivan Brugere, Xin Tian +4
Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many…
Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning
Yiping Ma, Jess Woods, Sebastian Angel +2
This paper introduces Flamingo, a system for secure aggregation of data across a large set of clients. In secure aggregation, a server sums up the private inputs of clients and obt…
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy
David Byrd, Vaikkunth Mugunthan, Antigoni Polychroniadou +1
Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted…
Paving the Way towards 800 Gbps Quantum-Secured Optical Channel Deployment in Mission-Critical Environments
Marco Pistoia, Omar Amer, Monik R. Behera +16
This article describes experimental research studies conducted towards understanding the implementation aspects of high-capacity quantum-secured optical channels in mission-critica…
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications
David Byrd, Antigoni Polychroniadou
Federated Learning enables a population of clients, working with a trusted server, to collaboratively learn a shared machine learning model while keeping each client's data within…
CryptoCredit: Securely Training Fair Models
Leo de Castro, Jiahao Chen, Antigoni Polychroniadou
When developing models for regulated decision making, sensitive features like age, race and gender cannot be used and must be obscured from model developers to prevent bias. Howeve…
Small Memory Robust Simulation of Client-Server Interactive Protocols over Oblivious Noisy Channels
T-H. Hubert Chan, Zhibin Liang, Antigoni Polychroniadou +1
We revisit the problem of low-memory robust simulation of interactive protocols over noisy channels. Haeupler [FOCS 2014] considered robust simulation of two-party interactive prot…