Abstract: Described are methods, devices and applications for learning noise distribution on information from any data processing method. In an embodiment of the described technology, a method includes determining an amount of shredding used in a shredding operation by which source data is converted into shredded data, and transferring the shredded data over an external network to a remote server for a data processing task. The shredding reduces the information content and incurs a limited degradation to an accuracy of the data processing task due to the shredding operation.

Abstract: Methods and systems that provide data privacy for implementing a neural network-based inference are described. A method includes injecting stochasticity into the data to produce perturbed data, wherein the injected stochasticity satisfies an ?-differential privacy criterion and transmitting the perturbed data to a neural network or to a partition of the neural network for inference.

Abstract: A personalized natural language processing system tokenizes a plurality of sets of raw text data to generate a plurality of sets of tokenized text data for the plurality of users, respectively. The tokenized text data includes a sequence of tokens corresponding to the raw text data, the tokens at least identifying distinct words or portions of words in the raw text. The system appends predetermined user-specific tokens to the sets of tokenized text data from the users, respectively. Each predetermined user-specific token corresponds to one of the users. The system processes the sets of tokenized text data using the NLP model in accordance with the appended predetermined user-specific tokens to predict a personalized classification for the sets of tokenized text data from each of the users, and outputs the personalized classifications of the tokenized text data for each of the users.

Abstract: Methods and systems that provide data privacy for implementing a neural network-based inference are described. A method includes injecting stochasticity into the data to produce perturbed data, wherein the injected stochasticity satisfies an F-differential privacy criterion and transmitting the perturbed data to a neural network or to a partition of the neural network for inference.

Abstract: Methods and systems that provide data privacy for implementing a neural network-based inference are described. A method includes injecting stochasticity into the data to produce perturbed data, wherein the injected stochasticity satisfies an ?-differential privacy criterion and transmitting the perturbed data to a neural network or to a partition of the neural network for inference.

Abstract: Methods and systems that provide data privacy for implementing a neural network-based inference are described. A method includes injecting stochasticity into the data to produce perturbed data, wherein the injected stochasticity satisfies an ?-differential privacy criterion and transmitting the perturbed data to a neural network or to a partition of the neural network for inference.

Abstract: Methods and systems that provide data privacy for implementing a neural network-based inference are described. A method includes injecting stochasticity into the data to produce perturbed data, wherein the injected stochasticity satisfies an ?-differential privacy criterion and transmitting the perturbed data to a neural network or to a partition of the neural network for inference.