ege-erdogan/unsplit
Supplementary code for the paper "UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning".
This project helps machine learning engineers and researchers evaluate the privacy risks in split learning systems. It takes a trained split learning model and demonstrates how an untrusted server can reconstruct sensitive input data, steal the client's model, and infer training labels. The output highlights vulnerabilities, showing that split learning may offer a false sense of security regarding data privacy.
No commits in the last 6 months.
Use this if you are designing or deploying a split learning system and need to understand its potential privacy weaknesses against an honest-but-curious server.
Not ideal if you are looking for methods to enhance the privacy of your split learning setup, as this project focuses on demonstrating vulnerabilities, not providing defenses.
Stars
14
Forks
3
Language
Python
License
—
Category
Last pushed
Nov 10, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ege-erdogan/unsplit"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
google/scaaml
SCAAML: Side Channel Attacks Assisted with Machine Learning
pralab/secml
A Python library for Secure and Explainable Machine Learning
Koukyosyumei/AIJack
Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
AI-SDC/SACRO-ML
Collection of tools and resources for managing the statistical disclosure control of trained...
oss-slu/mithridatium
Mithridatium is a research-driven project aimed at detecting backdoors and data poisoning in...