arXiv subjects

Chenkai Weng

Publications and source records attributed to Chenkai Weng.

5 recordsLinked to original sources

arXiv2026

Efficient Fuzzy PSI under One-Sided Assumptions

Fuzzy private set intersection (PSI) enables two parties to identify approximately matching elements between their input sets, where two elements are considered a match if their distance is at most a threshold $\delta$ under a given metric. Although substantial progress has been made, existing constructions for general Minkowski distances either rely on strong two-sided geometric separation assumptions or incur substantial overhead under one-sided assumptions. In this work, we present the first concretely efficient fuzzy PSI protocols for general $L_{p\in[1,\infty]}$ distances under one-sided assumptions, relying solely on lightweight symmetric-key primitives. Our constructions support both sender-sided and receiver-sided settings. We further study sparser input distributions and present more efficient protocols tailored to this case. To reduce the overhead scaling with $\delta$, we non-trivially incorporate prefix trie techniques into our protocols, achieving $O(\log\delta)$ complexity for general $L_{p\in[1,\infty]}$ distances for the first time, improving upon $O((\log\delta)^d)$ or $O(\delta)$ complexities of prior works. Extensive experiments, across a wide range of parameter settings, show that our protocols significantly outperform prior works under the same assumptions. Specifically, against van Baarsen and Pu (EUROCRYPT'24), our protocols achieve up to $239\times$ faster computation and up to $20\times$ lower communication. Against Dang et al. (CCS'25), we achieve up to $518\times$ speedup and up to $63\times$ communication reduction. Against Bui et al. (ASIACRYPT'25), we achieve up to $4818\times$ faster computation and up to $282\times$ lower communication.

cs.CR↗

arXiv2026

Efficient Fuzzy Private Set Intersection from Secret-shared OPRF

Private set intersection (PSI) enables a sender holding a set $Q$ of size $m$ and a receiver holding a set $W$ of size $n$ to securely compute the intersection $Q \cap W$. Fuzzy PSI (FPSI) is a PSI variant where the receiver learns the items $q \in Q$ for which there exists some $w \in W$ satisfying $\mathsf{dist}(q, w) \le \delta$ under a given distance metric. Although several FPSI works are proposed for $L_{p}$ distance metrics with $p \in [1, \infty]$, they either heavily rely on expensive homomorphic encryptions, or incur undesirable complexity, e.g., exponential to the element dimension, both of which lead to poor practical efficiency. In this work, we propose efficient FPSI protocols for $L_{p \in [1, \infty]}$ distance metrics, primarily leveraging significantly cheaper symmetric-key operations. Our protocols achieve linear communication and computation complexity in the set sizes $m,n$, the dimension $d$, and the distance threshold $\delta$. Our core building block is an oblivious programmable PRF with secret-shared outputs, which may be of independent interest. Furthermore, we incorporate a prefix technique that reduces the dependence on the distance threshold $\delta$ to logarithmic, which is particularly suitable for large $\delta$. We implement our FPSI protocols and compare them with state-of-the-art constructions. Experimental results demonstrate that our protocols consistently and significantly outperform existing works across all settings. Specifically, our protocols achieve a speedup of $12{\sim}145\times$ in running time and a reduction of $3{\sim}8\times$ in communication cost compared to Gao et al.~(ASIACRYPT'24) and a speedup of $9{\sim}80\times$ in running time and a reduction of $5{\sim}19\times$ in communication cost compared to Dang et al.~(CCS'25).

cs.CR↗

arXiv2026

ZKBoost: Zero-Knowledge Verifiable Training for XGBoost

Gradient boosted decision trees, particularly XGBoost, are among the most effective methods for tabular data. As deployment in sensitive settings increases, cryptographic guarantees of model integrity become essential. We present ZKBoost, the first zero-knowledge proof of training (zkPoT) protocol for XGBoost, enabling model owners to prove correct training on a committed dataset without revealing data or model parameters. Naively re-executing XGBoost training in ZK would incur prohibitive costs, primarily due to the oblivious partitioning of training samples and unknown tree splits. Moreover, previous work on ZKP of training and inference had subtle security issues, such as leakage of tree topology and soundness gaps allowing cheating model providers to deviate from the correct execution of training and inference. We make two key contributions to address these challenges: (1) a generic zkPoT template for XGBoost that can be instantiated with any general-purpose ZKP backend, significantly improving prover costs compared to naive re-execution of the training process; and (2) a VOLE-based instantiation that overcomes the security issues of previous ZK proofs of training at minimal costs. To maximize efficiency, we develop a fixed-point version of XGBoost, which is particularly well suited for efficient instantiation of ZKP, and show it matches standard XGBoost accuracy to within 1\% on real-world datasets.

cs.CR↗

arXiv2025

Collusion Resistant DNS With Private Information Retrieval

There has been a growing interest in Internet user privacy, demonstrated by the popularity of privacy-preserving products such as Telegram and Brave, and the widespread adoption of HTTPS. The Domain Name System (DNS) is a key component of Internet-based communication and its privacy has been neglected for years. Recently, DNS over HTTPS (DoH) has improved the situation by fixing the issue of in-path middleboxes. Further progress has been made with proxy-based solutions such as Oblivious DoH (ODoH), which separate a user's identity from their DNS queries. However, these solutions rely on non-collusion assumptions between DNS resolvers and proxies -- an assumption difficult to guarantee in practice. To address this, we explore integrating single-server Private Information Retrieval (PIR) into DNS to enable encrypted query processing without relying on trust assumptions. However, applying PIR to DNS is challenging due to its hierarchical nature -- particularly, interactions with recursive resolvers can still leak information. Navigating performance and privacy trade-offs, we propose PDNS, a DNS extension leveraging single-server PIR to strengthen privacy guarantees. We have implemented a prototype of PDNS and compared its performance against state-of-the-art solutions via trace-driven experiments. The results show that PDNS achieves acceptable performance (2x faster than DoH over Tor with similar privacy guarantees) and strong privacy guarantees today, mainly at the cost of its scalability, which specialized hardware for PIR can address in the near future.

cs.NI↗

arXiv2024

On Large Language Model Continual Unlearning

While large language models have demonstrated impressive performance across various domains and tasks, their security issues have become increasingly severe. Machine unlearning has emerged as a representative approach for model safety and security by removing the influence of undesired data on the target model. However, these methods do not sufficiently consider that unlearning requests in real-world scenarios are continuously emerging, especially in the context of LLMs, which may lead to accumulated model utility loss that eventually becomes unacceptable. Moreover, existing LLM unlearning methods often ignore previous data access limitations due to privacy concerns and copyright protection. Without previous data, the utility preservation during unlearning is much harder. To overcome these challenges, we propose the OOO framework that includes an Orthogonal low-rank adapter (LoRA) for continually unlearning requested data and an Out-Of-Distribution (OOD) detector to measure the similarity between input and unlearning data. The orthogonal LoRA achieves parameter disentanglement among continual unlearning requests. The OOD detector is trained with a novel contrastive entropy loss and utilizes a glocal-aware scoring mechanism. During inference, our OOO framework can decide whether and to what extent to load the unlearning LoRA based on the OOD detector's predicted similarity between the input and the unlearned knowledge. Notably, OOO's effectiveness does not rely on any retained data. We conducted extensive experiments on OOO and state-of-the-art LLM unlearning methods across three tasks and seven datasets. The results indicate that OOO consistently achieves the best unlearning effectiveness and utility preservation, especially when facing continuous unlearning requests. The source codes can be found at https://github.com/GCYZSL/O3-LLM-UNLEARNING.

cs.LG↗