To access this element change to forms mode OFF
Grant Award View - GA214449
MemberGuard: Protecting Machine Learning Privacy from Membership...
GA ID:
GA214449
Agency:
Australian Research Council
Approval Date:
14-Jan-2022
Publish Date:
20-Jan-2022
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
14-Jan-2022 to 31-Dec-2024
Value (AUD):
$450,000.00
(GST inclusive where applicable)
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 21/22 Discovery
Grant Program:
Discovery Projects
Grant Activity:
MemberGuard: Protecting Machine Learning Privacy from Membership Inference
Purpose:
Machine Learning has become a core part of many real-world applications. However, machine learning models are vulnerable to membership inference attacks. In these attacks, an adversary can infer if a given data record has been part of the model's training data. In this project, the team aims to develop new techniques that can be used to counter these attacks, such as 1) new analytical models for membership leakage, 2) new methods for susceptibility diagnosis, 3) new defences that leverage privacy and utility. Data-oriented services are estimated to be valuable assets in the future. These techniques can help Australia gain cutting edge advantage in machine learning security and privacy and protect its intellectual property on these services.
GO ID:
GO Title:
Discovery Projects for funding commencing in 2022
Internal Reference ID:
DP22 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
Swinburne University of Technology
Recipient ABN:
13 628 586 699
Grant Recipient Location
Suburb:
HAWTHORN
Town/City:
HAWTHORN
Postcode:
3122
State/Territory:
VIC
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
VIC
Postcode:
3122
Country:
AUSTRALIA