83 citations · 105 across the 25 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
: 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…
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…
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…
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…
More is Less: Perfectly Secure Oblivious Algorithms in the Multi-Server Setting
T-H. Hubert Chan, Jonathan Katz, Kartik Nayak +2
The problem of Oblivious RAM (ORAM) has traditionally been studied in a single-server setting, but more recently the multi-server setting has also been considered. Yet it is still…