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

ProvGuard: Detecting SDN Control Policy Manipulation via Contextual Semantics...

Ziwen Liu (Beihang University), Jian Mao (Beihang University; Tianmushan Laboratory; Hangzhou Innovation Institute, Beihang University), Jun Zeng (National University of Singapore), Jiawei Li (Beihang University; National University of Singapore), Qixiao Lin (Beihang University), Jiahao Liu (National University of Singapore), Jianwei Zhuge (Tsinghua University; Zhongguancun Laboratory), Zhenkai Liang (National University of Singapore)

Read More

Statically Discover Cross-Entry Use-After-Free Vulnerabilities in the Linux Kernel

Hang Zhang (Indiana University Bloomington), Jangha Kim (The Affiliated Institute of ETRI, ROK), Chuhong Yuan (Georgia Institute of Technology), Zhiyun Qian (University of California, Riverside), Taesoo Kim (Georgia Institute of Technology)

Read More

Safety Misalignment Against Large Language Models

Yichen Gong (Tsinghua University), Delong Ran (Tsinghua University), Xinlei He (Hong Kong University of Science and Technology (Guangzhou)), Tianshuo Cong (Tsinghua University), Anyu Wang (Tsinghua University), Xiaoyun Wang (Tsinghua University)

Read More

LLMPirate: LLMs for Black-box Hardware IP Piracy

Vasudev Gohil (Texas A&M University), Matthew DeLorenzo (Texas A&M University), Veera Vishwa Achuta Sai Venkat Nallam (Texas A&M University), Joey See (Texas A&M University), Jeyavijayan Rajendran (Texas A&M University)

Read More