Training data poisoning detection
AssigneeHewlett Packard Enterprise Development LP
InventorsOmer Uretzky, Gil Barash, Amir Idar
Life of this patent
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Abstract
In some examples, a system receives a plurality of training samples of a training data set for a machine learning model, where each training sample of the plurality of training samples comprises a plurality of features. The system determines quantities of changes made to respective features of the plurality of features, computes a score representing an integrity of the training data set based on the quantities, and detects poisoning of the training data set based on the score.
Claims18
A non-transitory machine-readable storage medium comprising instructions that upon execution cause a system to:
- receive a plurality of training samples of a training data set for a machine learning model, wherein each training sample of the plurality of training samples comprises a plurality of features, the plurality of training samples is included in replicated training data provided by a data replication manager that replicates data writes to a storage system, and the data writes are replicated to a persistent memory;
- determine quantities of changes made to respective features of the plurality of features;
- compute a score representing an integrity of the training data set based on the quantities;
- detect poisoning of the training data set based on the score;
- identify a time point at which the poisoning of the training data set is detected;
- and
- produce, from the replicated training data, an uncorrupted version of the training data set based on the identified time point.
A system comprising:
- a processor;
- and
- a non-transitory storage medium storing instructions executable on the processor to:
- receive an input collection of training samples for a training data set, the training data set used for training a machine learning model, wherein each training sample of the input collection of training samples comprises a plurality of features, and the training samples are included in replicated training data provided by a data replication manager that replicates data writes targeting a storage system to a persistent memory;
- determine quantities of outliers in values of respective features of the plurality of features;
- compute a score representing an integrity of the training data set based on the quantities;
- detect poisoning of the training data set based on the score;
- identify a time point at which the poisoning of the training data set is detected;
- and
- produce, from the replicated training data, an uncorrupted version of the training data set based on the identified time point.
A method comprising:
- receiving an input collection of training samples for a training data set, the training data set used for training a machine learning model, wherein each training sample of the input collection of training samples comprises a plurality of features, and the training samples are included in replicated training data provided by a data replication manager that replicates data writes targeting a storage system to a persistent memory;
- determining, by a system comprising a hardware processor, quantities of changes made to respective features of the plurality of features;
- determining, by the system, quantities of outliers in values of the respective features;
- computing, by the system, a score representing an integrity of the training data set based on the quantities of changes and the quantities of outliers;
- detecting, by the system, poisoning of the training data set based on the score;
- identifying, by the system, a time point at which the poisoning of the training data set is detected;
- and
- producing, by the system from the replicated training data, an uncorrupted version of the training data set based on the identified time point.
Dependencies are inferred from the claim text.