Eduardo Chielle (New York University Abu Dhabi), Michail Maniatakos (New York University Abu Dhabi)

A Private Set Intersection (PSI) protocol is a cryptographic method allowing two parties, each with a private set, to determine the intersection of their sets without revealing any information about their entries except for the intersection itself. While extensive research has focused on PSI protocols, most studies have centered on scenarios where two parties possess sets of similar sizes, assuming a semi-honest threat model.
However, when the sizes of the parties' sets differ significantly, a generalized solution tends to underperform compared to a specialized one, as recent research has demonstrated. Additionally, conventional PSI protocols are typically designed for a single execution, requiring the entire protocol to be re-executed for each set intersection. This approach is suboptimal for applications such as URL denylisting and email filtering, which may involve multiple set intersections of small sets against a large set (e.g., one for each email received).
In this study, we propose a novel PSI protocol optimized for the recurrent setting where parties have unbalanced set sizes. We implement our protocol using Levelled Fully Homomorphic Encryption and Cuckoo hashing, and introduce several optimizations to ensure real-time performance. By utilizing the Microsoft SEAL library, we demonstrate that our protocol can perform private set intersections in 20 ms and 240 ms on 10 Gbps and 100 Mbps networks, respectively.
Compared to existing solutions, our protocol offers significant improvements, reducing set intersection times by one order of magnitude on slower networks and by two orders of magnitude on faster networks.

View More Papers

LightAntenna: Characterizing the Limits of Fluorescent Lamp-Induced Electromagnetic Interference

Fengchen Yang (Zhejiang University), Wenze Cui (Zhejiang University), Xinfeng Li (Zhejiang University), Chen Yan (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

Read More

Speak Up, I’m Listening: Extracting Speech from Zero-Permission VR...

Derin Cayir (Florida International University), Reham Mohamed Aburas (American University of Sharjah), Riccardo Lazzeretti (Sapienza University of Rome), Marco Angelini (Link Campus University of Rome), Abbas Acar (Florida International University), Mauro Conti (University of Padua), Z. Berkay Celik (Purdue University), Selcuk Uluagac (Florida International University)

Read More

ReThink: Reveal the Threat of Electromagnetic Interference on Power...

Fengchen Yang (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Zihao Dan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Kaikai Pan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Chen Yan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Xiaoyu Ji (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Wenyuan Xu (Zhejiang University; ZJU…

Read More

Diffence: Fencing Membership Privacy With Diffusion Models

Yuefeng Peng (University of Massachusetts Amherst), Ali Naseh (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

Read More