Anonymized time-series generation from recurrent neural networks
AssigneeInternational Business Machines Corporation
InventorsSupriyo Chakraborty, Mudhakar Srivatsa
Life of this patent
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Abstract
An output time-series of a cell of a neural network is captured. A subset of a set of data points of the output time-series is consolidated into a singular data point. The singular data point is fitted in a data representation to form a quantified aggregated data point. The quantified aggregated data point is included in an intermediate time-series. Using the intermediate time-series as an input at an intermediate layer of the neural network, an anonymized output time-series is produced from the neural network.
Claims20
A method comprising:
- capturing, using a processor and a memory, an output time-series of a cell of a neural network;
- consolidating a subset of a set of data points of the output time-series into a singular data point;
- fitting the singular data point in a data representation, to form a quantified aggregated data point;
- and
- producing from the neural network, using an intermediate time-series as an input at an intermediate layer of the neural network, an anonymized output time-series, the intermediate time-series comprising the quantified aggregated data point.
A computer usable program product comprising one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices, the stored program instructions comprising:
- program instructions to capture, using a processor and a memory, an output time-series of a cell of a neural network;
- program instructions to consolidate a subset of a set of data points of the output time-series into a singular data point;
- program instructions to fit the singular data point in a data representation, to form a quantified aggregated data point;
- and
- program instructions to produce from the neural network, using an intermediate time-series as an input at an intermediate layer of the neural network, an anonymized output time-series, the intermediate time-series comprising the quantified aggregated data point.
A computer system comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising:
- program instructions to capture an output time-series of a cell of a neural network;
- program instructions to consolidate a subset of a set of data points of the output time-series into a singular data point;
- program instructions to fit the singular data point in a data representation, to form a quantified aggregated data point;
- and
- program instructions to produce from the neural network, using an intermediate time-series as an input at an intermediate layer of the neural network, an anonymized output time-series, the intermediate time-series comprising the quantified aggregated data point.
Dependencies are inferred from the claim text.