1 citations · 1 across the 10 of their papers we have counts for
11 papers
Hoss: Fast Oblivious Semantic Search with Heterogeneous GPU-CPU-TEE Architecture
Jianzhang Du, Weijie Huang, Chenghong Wang +4
Semantic search is widely deployed in modern AI systems, but protecting both data contents and access patterns remains challenging. The current state-of-the-art system, Compass, ac…
Secret-Protected Evolution for Differentially Private Synthetic Text Generation
Tianze Wang, Zhaoyu Chen, Jian Du +3
Text data has become extremely valuable on large language models (LLMs) and even lead to general artificial intelligence (AGI). A lot of high-quality text in the real world is priv…
MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools
Wenhao Wang, Peizhi Niu, Zhao Xu +8
Large Language Models (LLMs) increasingly rely on external tools to perform complex, realistic tasks, yet their ability to utilize the rapidly expanding Model Contextual Protocol (…
TokenShapley: Token Level Context Attribution with Shapley Value
Yingtai Xiao, Yuqing Zhu, Sirat Samyoun +3
Large language models (LLMs) demonstrate strong capabilities in in-context learning, but verifying the correctness of their generated responses remains a challenge. Prior work has…
PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection
Jian Du, Haohao Qian, Shikun Zhang +6
This paper tackles the challenging and practical problem of multi-identifier private user profile matching for privacy-preserving ad measurement, a cornerstone of modern advertisin…
When Focus Enhances Utility: Target Range LDP Frequency Estimation and Unknown Item Discovery
Bo Jiang, Wanrong Zhang, Donghang Lu +2
Local Differential Privacy (LDP) protocols enable the collection of randomized client messages for data analysis, without the necessity of a trusted data curator. Such protocols ha…
Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach
Bo Jiang, Wanrong Zhang, Donghang Lu +3
Data engineering often requires accuracy (utility) constraints on results, posing significant challenges in designing differentially private (DP) mechanisms, particularly under str…
Differentially Private Learned Indexes
Jianzhang Du, Tilak Mudgal, Rutvi Rahul Gadre +2
In this paper, we address the problem of efficiently answering predicate queries on encrypted databases, those secured by Trusted Execution Environments (TEEs), which enable untrus…
Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy
Yingtai Xiao, Jian Du, Shikun Zhang +4
Online advertising is a cornerstone of the Internet ecosystem, with advertising measurement playing a crucial role in optimizing efficiency. Ad measurement entails attributing desi…
Budget Recycling Differential Privacy
Bo Jiang, Jian Du, Sagar Sharma +1
Differential Privacy (DP) mechanisms usually {force} reduction in data utility by producing "out-of-bound" noisy results for a tight privacy budget. We introduce the Budget Recycli…
AnonPSI: An Anonymity Assessment Framework for PSI
Bo Jiang, Jian Du, Qiang Yan
Private Set Intersection (PSI) is a widely used protocol that enables two parties to securely compute a function over the intersected part of their shared datasets and has been a s…