SecVM

SecVM

Privacy-preserving classification

SecVM trains a Support Vector Machine (SVM) classifier in a federated setting where the central server is considered untrusted. User data is kept local and only encrypted gradient updates are sent to the server, using secret-sharing and homomorphic-encryption-inspired techniques to prevent the server from learning individual data points or their labels.

DecentralizedDistributed Learning
Maturity
PrototypeIntermediateMature
Support
C4DT
Inactive
Lab
Unknown

Data Science Lab

Data Science Lab
Robert West

Prof. Robert West

Our research aims to make sense of large amounts of data. Frequently, the data we analyze is collected on the Web, e.g., using server logs, social media, wikis, online news, online games, etc. We distill heaps of raw data into meaningful insights by developing and applying algorithms and techniques in areas including social and information network analysis, machine learning, computational social science, data mining, natural language processing, and human computation.

This page was last edited on 2024-04-16.