18 citations · 35 across the 10 of their papers we have counts for
12 papers
GneissWeb: Preparing High Quality Data for LLMs at Scale
Hajar Emami Gohari, Swanand Ravindra Kadhe, Syed Yousaf Shah +29
Data quantity and quality play a vital role in determining the performance of Large Language Models (LLMs). High-quality data, in particular, can significantly boost the LLM's abil…
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms
Tianshu Zhang, Changchang Liu, Wei-Han Lee +2
This paper studies a new task of federated learning (FL) for semantic parsing, where multiple clients collaboratively train one global model without sharing their semantic parsing…
Joint Coreset Construction and Quantization for Distributed Machine Learning
Hanlin Lu, Changchang Liu, Shiqiang Wang +4
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication…
AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy
Linkang Du, Zhikun Zhang, Shaojie Bai +4
For protecting users' private data, local differential privacy (LDP) has been leveraged to provide the privacy-preserving range query, thus supporting further statistical analysis.…
Communication-efficient k-Means for Edge-based Machine Learning
Hanlin Lu, Ting He, Shiqiang Wang +5
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine lear…
Sharing Models or Coresets: A Study based on Membership Inference Attack
Hanlin Lu, Changchang Liu, Ting He +2
Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different appr…
Overcoming Noisy and Irrelevant Data in Federated Learning
Tiffany Tuor, Shiqiang Wang, Bong Jun Ko +2
Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and comm…
Blind De-anonymization Attacks using Social Networks
Wei-Han Lee, Changchang Liu, Shouling Ji +2
It is important to study the risks of publishing privacy-sensitive data. Even if sensitive identities (e.g., name, social security number) were removed and advanced data perturbati…
RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release
Thee Chanyaswad, Changchang Liu, Prateek Mittal
A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data. To overcome this challenge, we utilize the…
Quantification of De-anonymization Risks in Social Networks
Wei-Han Lee, Changchang Liu, Shouling Ji +2
The risks of publishing privacy-sensitive data have received considerable attention recently. Several de-anonymization attacks have been proposed to re-identify individuals even if…
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications
Changchang Liu, Supriyo Chakraborty, Prateek Mittal
Personal sensory data is used by context-aware mobile applications to provide utility. However, the same data can also be used by an adversary to make sensitive inferences about a…
LinkMirage: How to Anonymize Links in Dynamic Social Systems
Changhchang Liu, Prateek Mittal
Social network based trust relationships present a critical foundation for designing trustworthy systems, such as Sybil defenses, secure routing, and anonymous/censorshipresilient…