The School of Computer Science is pleased to present…
PPPCT: Privacy-Preserving Framework for Parallel Clustering Transcriptomics Data
PhD Dissertation Proposal by: Ali Abbasi Tadi
Date: Thursday, July 13th, 2023
Time: 11:00 AM
Location: Essex Hall Room 122
Reminders: 1. Two-part attendance mandatory (sign-in sheet, QR Code) 2. Arrive 5-10 minutes prior to event starting - LATECOMERS WILL NOT BE ADMITTED. Note that due to demand, if the room has reached capacity, even if you are "early" admission is not guaranteed. 3. Please be respectful of the presenter by NOT knocking on the door for admittance once the door has been closed whether the presentation has begun or not (If the room is at capacity, overflow is not permitted (ie. sitting on floors) as this is a violation of the Fire Safety code). 4. Be respectful of the decision of the advisor/host of the event if you are not given admittance. The School of Computer Science has numerous events occurring soon
Abstract:
Single-cell transcriptomics data allows for revealing important information about patients. Although extensive work has been done on improving the clustering quality of such data, the domain still faces critical challenges. In particular, the privacy aspects of high-dimensional single-cell transcriptomics datasets are of pivotal importance. In this regard, an efficient method for single-cell clustering should consider the following: 1) high dimensionality of the datasets, 2) high quality of the clustering, 3) privacy of the data and models, and 4) reasonable computation time for large datasets. Our work proposes a novel, fast, privacy-preserving, scalable approach for clustering single-cell datasets. The method employs the map-reduce method to parallelize clustering to address intensive calculation problems. SGX processors handle the scheme's sensitive code and data for secure processing. Experimental results show that our approach not just preserves privacy but also outperforms current widely used methods with respect to efficiency and computation time.
Thesis Committee:
Internal Reader: Dr. Luis Rueda
Internal Reader: Dr. Saeed Samet
External Reader: Dr. Ning Zhang
Advisor: Dr. Dima Alhadidi
PhD Dissertation Proposal Announcement