37 citations · 74 across the 13 of their papers we have counts for
13 papers
Don't Stir the Pot! Authorized Vector Data Retrieval via Access-Aware Indexing
Shanshan Han, Vishal Chakraborty, Sharad Mehrotra
Vector databases increasingly enforce role-based access control, where each top-k approximate nearest neighbor query must return only vectors the querying role is authorized to acc…
Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM Inferences
Shanshan Han, Salman Avestimehr, Chaoyang He
We present Wildflare GuardRail, a guardrail pipeline designed to enhance the safety and reliability of Large Language Model (LLM) inferences by systematically addressing risks acro…
Alopex: A Computational Framework for Enabling On-Device Function Calls with LLMs
Yide Ran, Zhaozhuo Xu, Yuhang Yao +9
The rapid advancement of Large Language Models (LLMs) has led to their increased integration into mobile devices for personalized assistance, which enables LLMs to call external AP…
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency
Yuhang Yao, Han Jin, Alay Dilipbhai Shah +7
Large language models (LLMs) have surged in popularity and are extensively used in commercial applications, where the efficiency of model serving is crucial for the user experience…
TorchOpera: A Compound AI System for LLM Safety
Shanshan Han, Zijian Hu, Alay Dilipbhai Shah +5
We introduce TorchOpera, a compound AI system for enhancing the safety and quality of prompts and responses for Large Language Models. TorchOpera ensures that all user prompts are…
LLM Multi-Agent Systems: Challenges and Open Problems
Shanshan Han, Qifan Zhang, Weizhao Jin +1
This paper explores multi-agent systems and identify challenges that remain inadequately addressed. By leveraging the diverse capabilities and roles of individual agents, multi-age…
Hiding Access-pattern is Not Enough! Veil: A Storage and Communication Efficient Volume-Hiding Algorithm
Shanshan Han, Vishal Chakraborty, Michael Goodrich +2
This paper addresses volume leakage (i.e., leakage of the number of records in the answer set) when processing keyword queries in encrypted key-value (KV) datasets. Volume leakage,…
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
Shanshan Han, Wenxuan Wu, Baturalp Buyukates +4
Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations…
Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data
Jiaqi Shao, Shanshan Han, Chaoyang He +1
Federated heavy-hitter analytics involves the identification of the most frequent items within distributed data. Existing methods for this task often encounter challenges such as c…
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
Shanshan Han, Baturalp Buyukates, Zijian Hu +13
This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defens…
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Weizhao Jin, Yuhang Yao, Shanshan Han +5
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated…
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain
Baturalp Buyukates, Chaoyang He, Shanshan Han +5
We consider a project (model) owner that would like to train a model by utilizing the local private data and compute power of interested data owners, i.e., trainers. Our goal is to…
Federated Analytics: A survey
Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Shanshan Han +4
Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g.,…