Securing Data Analytics on Intel SGX using Randomization
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
App
Enclave
Include
Makefile
README.md
private_key.pem
public_key.pem

README.md


Securing Data Analytics on SGX with Randomization

Details can be found in here.

Project:

  1. App/App.cpp contains the untrusted code.
  2. Enclave/Analytics/ contain the data analytics used in various experiments in the paper.

Settings (in App/App.cpp):

  1. basedir: Directory of input data files.
  2. Experiments settings on chunk/minibatch size, proportion of dummy data instances, number of clusters in K-Means clustering.
  3. Class label prediction analytics include Decision Tree, Naive Bayes and K-means. Each classifier can be run by choosing appropriate code, as given in App.cpp under ocall_manager function.

How to Build/Execute the Code

  1. Install Intel(R) SGX SDK for Linux* OS
  2. Specify data directory in basedir of App/App.cpp.
  3. Also specify appropriate settings in App/App.cpp file.
  4. Build the project with the prepared Makefile: a. Hardware Mode, Debug build: $ make SGX_MODE=HW SGX_DEBUG=1 b. Hardware Mode, Pre-release build: $ make SGX_MODE=HW SGX_PRERELEASE=1 c. Hardware Mode, Release build: $ make SGX_MODE=HW d. Simulation Mode, Debug build: $ make SGX_DEBUG=1 e. Simulation Mode, Pre-release build: $ make SGX_PRERELEASE=1 f. Simulation Mode, Release build: $ make
  5. Execute the binary directly: $ ./app