Abstract:
A method comprises receiving in a governor device, from a plurality of data owner devices, metadata for one or more datasets maintained by the plurality of data owner devices, registering the metadata for the one or more datasets with the governor device, in response to a request from an aggregator, providing at least a portion of the metadata for the one or more datasets to the aggregator, receiving, from the aggregator, a compute plan to be implemented by the plurality of data owner devices, distributing at least a portion of the compute plan to the plurality of data owner devices, in response to receiving, from the plurality of data owner devices, a verification report and a certification for an enclave, binding the enclave to a host device, and providing the compute plan to the plurality of data owner devices.
Abstract:
Embodiments discussed herein may be generally directed to systems and techniques to generate a quality score based on an observation and an action caused by an actor agent during a testing phase. Embodiments also include determining a temporal difference between the quality score and a previous quality score based on a previous observation and a previous action, determining whether the temporal difference exceeds a threshold value, and generating an attack indication in response to determining the temporal difference exceeds the threshold value.
Abstract:
An example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to access a first set of samples associated with a diffusion model, the first set of samples including a plurality of input data samples, generate a representation of the first set of samples, sample the representation of the first set of samples to generate a representation of a second set of samples, and generate the second set of samples from the representation of the second set of samples, the second set of samples including a plurality of output data samples, an output data sample corresponding to an input data sample and being different from the corresponding input data sample.
Abstract:
A method comprises receiving in a governor device, from a plurality of data owner devices, metadata for one or more datasets maintained by the plurality of data owner devices, registering the metadata for the one or more datasets with the governor device, in response to a request from an aggregator, providing at least a portion of the metadata for the one or more datasets to the aggregator, receiving, from the aggregator, a compute plan to be implemented by the plurality of data owner devices, distributing at least a portion of the compute plan to the plurality of data owner devices, in response to receiving, from the plurality of data owner devices, a verification report and a certification for an enclave, binding the enclave to a host device, and providing the compute plan to the plurality of data owner devices.
Abstract:
The present invention discloses a secure ML pipeline to improve the robustness of ML models against poisoning attacks and utilizing data provenance as a tool. Two components are added to the ML pipeline, a data quality pre-processor, which filters out untrusted training data based on provenance derived features and an audit post-processor, which localizes the malicious source based on training dataset analysis using data provenance.
Abstract:
Technologies are provided in embodiments to manage an authentication confirmation score. Embodiments are configured to identify, in absolute session time, a beginning time and an ending time of an interval of an active user session on a client. Embodiments are also configured to determine a first value representing a first subset of a set of prior user sessions, where the prior user sessions of the first subset were active for at least as long as the beginning time. Embodiments can also determine a second value representing a second subset of the set of prior user sessions, where the prior user sessions of the second subset were active for at least as long as the ending time. Embodiments also determine, based on the first and second values, a decay rate for the authentication confidence score of the active user session. In some embodiments, the set is based on context attributes.
Abstract:
Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.
Abstract:
Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.
Abstract:
Technologies are provided in embodiments to manage an authentication confirmation score. Embodiments are configured to identify, in absolute session time, a beginning time and an ending time of an interval of an active user session on a client. Embodiments are also configured to determine a first value representing a first subset of a set of prior user sessions, where the prior user sessions of the first subset were active for at least as long as the beginning time. Embodiments can also determine a second value representing a second subset of the set of prior user sessions, where the prior user sessions of the second subset were active for at least as long as the ending time. Embodiments also determine, based on the first and second values, a decay rate for the authentication confidence score of the active user session. In some embodiments, the set is based on context attributes.
Abstract:
Technologies are provided in embodiments to manage an authentication confirmation score. Embodiments are configured to identify, in absolute session time, a beginning time and an ending time of an interval of an active user session on a client. Embodiments are also configured to determine a first value representing a first subset of a set of prior user sessions, where the prior user sessions of the first subset were active for at least as long as the beginning time. Embodiments can also determine a second value representing a second subset of the set of prior user sessions, where the prior user sessions of the second subset were active for at least as long as the ending time. Embodiments also determine, based on the first and second values, a decay rate for the authentication confidence score of the active user session. In some embodiments, the set is based on context attributes.