Machine learning model validation and authentication
AssigneeKoninklijke Philips N.V.
InventorsShawn Arie Peter Stapleton, Amir Mohammad Tahmasebi Maraghoosh
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Abstract
The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.
Claims20
A method implemented using one or more processors, comprising:
- providing an encryption key that is associated with a particular entity, wherein the particular entity has access to a machine learning model that is trained to generate one or more outputs based on data applied across a plurality of inputs;
- encrypting one or more parameters of the trained machine learning model using the encryption key;
- encrypting input data to be applied as input across the trained machine learning model;
- applying the encrypted input data as input across the encrypted trained machine learning model to generate encrypted output;
- decrypting the encrypted output using a decryption key to generate decrypted output;
- analyzing the decrypted output using the decryption key to determine that one or more of the parameters of the trained machine learning model have been compromised;
- and
- causing one or more computing devices to provide output that indicates that the one or more parameters of the trained machine learning model have been compromised.
A system comprising one or more processors and memory storing instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to perform the following operations:
- providing an encryption key that is associated with a particular entity, wherein the particular entity has access to a machine learning model that is trained to generate one or more outputs based on data applied across a plurality of inputs;
- encrypting one or more parameters of the trained machine learning model using the encryption key;
- encrypting input data to be applied as input across the trained machine learning model;
- applying the encrypted input data as input across the encrypted trained machine learning model to generate encrypted output;
- decrypting the encrypted output using a decryption key to generate decrypted output;
- analyzing the decrypted output using the decryption key to determine that one or more of the parameters of the trained machine learning model have been compromised;
- and
- causing one or more computing devices to provide output that indicates that the one or more parameters of the trained machine learning model have been compromised.
At least one non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:
- providing an encryption key that is associated with a particular entity, wherein the particular entity has access to a machine learning model that is trained to generate one or more outputs based on data applied across a plurality of inputs;
- encrypting one or more parameters of the trained machine learning model using the encryption key;
- encrypting input data to be applied as input across the trained machine learning model;
- applying the encrypted input data as input across the encrypted trained machine learning model to generate encrypted output;
- decrypting the encrypted output using a decryption key to generate decrypted output;
- analyzing the decrypted output using the decryption key to determine that one or more of the parameters of the trained machine learning model have been compromised;
- and
- causing one or more computing devices to provide output that indicates that the one or more parameters of the trained machine learning model have been compromised.
Dependencies are inferred from the claim text.