Side-channel protection for machine learning models
AssigneeQUALCOMM Incorporated
InventorsCong CHEN, Jonathan PETIT, Jean-Philippe MONTEUUIS
Life of this patent
Estimating the term…
Abstract
Systems and techniques are described herein for attack mitigation. For instance, a process can include allocating a first masked gadget, of a plurality of masked gadgets, for executing a first machine learning (ML) model; allocating a second masked gadget, of the plurality of masked gadgets, for executing a second ML model; and using the first masked gadget with the first ML model concurrently with the second masked gadget with the second ML model.
Claims20
An apparatus for attack mitigation, the apparatus comprising:
- a memory;
- and
- a processor coupled to the memory and configured to:
- allocate a first masked gadget, of a plurality of masked gadgets, for executing a first machine learning (ML) model;
- allocate a second masked gadget, of the plurality of masked gadgets, for executing a second ML model;
- and
- use the first masked gadget with the first ML model concurrently with the second masked gadget with the second ML model.
A method for attack mitigation, comprising:
- allocating a first masked gadget, of a plurality of masked gadgets, for executing a first machine learning (ML) model;
- allocating a second masked gadget, of the plurality of masked gadgets, for executing a second ML model;
- and
- using the first masked gadget with the first ML model concurrently with the second masked gadget with the second ML model.
A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to:
- allocate a first masked gadget, of a plurality of masked gadgets, for executing a first machine learning (ML) model;
- allocate a second masked gadget, of the plurality of masked gadgets, for executing a second ML model;
- and
- use the first masked gadget with the first ML model concurrently with the second masked gadget with the second ML model.
Dependencies are inferred from the claim text.