Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage medium, for machine unlearning. In one aspect a method includes receiving a request to remove a client dataset from a machine learning model, the model being associated with noise sensitivities determined during training of the model on respective client datasets including the client; and in response to receiving the request: identifying, from stored noise sensitivities of the client, a most recent training iteration that produced a noise sensitivity that is below a predetermined threshold that is based on a noise standard deviation and predefined target privacy parameters; updating parameters of the model, comprising adding noise to model parameters for the most recent training iteration; and performing subsequent iterations of training of the model, wherein the model is initialized with the updated parameters and the subsequent iterations train the model on datasets excluding the dataset owned by the client.

Abstract: Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support improved watermarking and fingerprinting of a shared dataset. To illustrate, clustering may be performed on the dataset using initial clustering parameters (e.g., a secret key) to assign each record (e.g., attribute) of the dataset to one of multiple clusters. The secret key may be selected by a user or determined automatically based on the clustering algorithm. After the clustering, the records of each cluster may be selected for embedding a portion of fingerprint data based on one or more security parameters (e.g., a hash function, priority values, even/or selection, etc.). The selected records (or portions thereof) may be replaced with corresponding portions of the fingerprint data to embed the fingerprint data within different records as watermarking. Aspects also include analyzing a dataset to verify whether watermarking is present and to extract a fingerprint.

Abstract: Systems and methods for smart incentivization for achieving collaborative machine learning are disclosed. A system receives local model parameters from plurality of client devices in a network, for global model corresponding to collaborative machine learning. The system determines an optimum score for each client device using pre-trained Conditional Variational Auto Encoder (CVAE), based on local model parameter. The system computes contribution score for each client device by determining relative distance value of optimum score corresponding to each client device with optimum score corresponding to another client device from the plurality of client devices, and a global model optimum score of global model. The system updates global model with local model parameter received from the selected set of client devices of the plurality of client devices corresponding to good class, average class, and bad class.

Abstract: A system and method for facilitating a rule-based anonymization of an original dataset is disclosed. The system may include a processor including a data privacy evaluator and a rules engine. The data privacy evaluator may receive at least one anonymized dataset corresponding to a predefined strategy of anonymization. The at least one anonymized dataset may include a variation from the original dataset by at least one of a privacy metric and a consistency metric. The data privacy evaluator may evaluate the at least one anonymized dataset and may generate a final output value based on a first output and a second output. The processor may assess the final output value with respect to a predefined threshold through the rules engine. If the final output value may be equal or higher than the predefined threshold, the system may permit an access to the anonymized dataset.

Abstract: Systems, apparatuses, and methods are directed toward determining emotional states of a subject. For example, the systems, apparatuses, and methods determine one or more previous characteristics of a pupil of an eye based on image data from eye tracking sensors, determine one or more current characteristics of the pupil based on image data from the eye tracking sensors, where the one or more current characteristics include one or more of a current position of the pupil or a current size of the pupil, track movements, based on image data from the eye tracking sensors, in a position of the pupil and changes in a size of the pupil over a time period, automatically predict an emotional state based on a comparison between the one or more previous characteristics and the one or more current characteristics, the tracked movements and the tracked changes in the size and adjust a parameter of one or more hardware devices based on the predicted emotional state.

Abstract: A transaction platform may provide access, to a distributed shared data structure, to a user device and a service provider device. The transaction platform may store, in the distributed shared data structure, user information associated with a user and service information associated with a service provider. The transaction platform may process a transaction between the user and the service provider by using a first set of processing nodes to generate a first transaction result and a second set of processing nodes is to generate a second transaction result. The transaction platform may determine whether the first transaction result and the second transaction result are the same, and may store, based on determining that the first transaction result and the second transaction result match, a smart contract, associated with the transaction, in a distributed ledger.

Abstract: Systems, apparatuses, and methods are directed toward determining emotional states of a subject. For example, the systems, apparatuses, and methods determine one or more previous characteristics of a pupil of an eye based on image data from eye tracking sensors, determine one or more current characteristics of the pupil based on image data from the eye tracking sensors, where the one or more current characteristics include one or more of a current position of the pupil or a current size of the pupil, track movements, based on image data from the eye tracking sensors, in a position of the pupil and changes in a size of the pupil over a time period, automatically predict an emotional state based on a comparison between the one or more previous characteristics and the one or more current characteristics, the tracked movements and the tracked changes in the size and adjust a parameter of one or more hardware devices based on the predicted emotional state.

Abstract: A transaction platform may provide access, to a distributed shared data structure, to a user device and a service provider device. The transaction platform may store, in the distributed shared data structure, user information associated with a user and service information associated with a service provider. The transaction platform may process a transaction between the user and the service provider by using a first set of processing nodes to generate a first transaction result and a second set of processing nodes is to generate a second transaction result. The transaction platform may determine whether the first transaction result and the second transaction result are the same, and may store, based on determining that the first transaction result and the second transaction result match, a smart contract, associated with the transaction, in a distributed ledger.