Abstract: A method for performing privacy-preserving or secure multi-party computations enables multiple parties to collaborate to produce a shared result while preserving the privacy of input data contributed by individual parties. The method can produce a result with a specified high degree of precision or accuracy in relation to an exactly accurate plaintext (non-privacy-preserving) computation of the result, without unduly burdensome amounts of inter-party communication. The multi-party computations can include a Fourier series approximation of a continuous function or an approximation of a continuous function using trigonometric polynomials, for example, in training a machine learning classifier using secret shared input data.

Abstract: A method for performing privacy-preserving or secure multi-party computations enables multiple parties to collaborate to produce a shared result while preserving the privacy of input data contributed by individual parties. The method can produce a result with a specified high degree of precision or accuracy in relation to an exactly accurate plaintext (non-privacy-preserving) computation of the result, without unduly burdensome amounts of inter-party communication. The multi-party computations can include a Fourier series approximation of a continuous function or an approximation of a continuous function using trigonometric polynomials, for example, in training a machine learning classifier using secret shared input data.

Abstract: A method for performing privacy-preserving or secure multi-party computations enables multiple parties to collaborate to produce a shared result while preserving the privacy of input data contributed by individual parties. The method can produce a result with a specified high degree of precision or accuracy in relation to an exactly accurate plaintext (non-privacy-preserving) computation of the result, without unduly burdensome amounts of inter-party communication. The multi-party computations can include a Fourier series approximation of a continuous function or an approximation of a continuous function using trigonometric polynomials, for example, in training a machine learning classifier using secret shared input data.

Abstract: A method for performing privacy-preserving or secure multi-party computations enables multiple parties to collaborate to produce a shared result while preserving the privacy of input data contributed by individual parties. The method can produce a result with a specified high degree of precision or accuracy in relation to an exactly accurate plaintext (non-privacy-preserving) computation of the result, without unduly burdensome amounts of inter-party communication. The multi-party computations can include a Fourier series approximation of a continuous function or an approximation of a continuous function using trigonometric polynomials, for example, in training a machine learning classifier using secret shared input data.

Abstract: A method for performing privacy-preserving or secure multi-party computations enables multiple parties to collaborate to produce a shared result while preserving the privacy of input data contributed by individual parties. The method can produce a result with a specified high degree of precision or accuracy in relation to an exactly accurate plaintext (non-privacy-preserving) computation of the result, without unduly burdensome amounts of inter-party communication. The multi-party computations can include a Fourier series approximation of a continuous function or an approximation of a continuous function using trigonometric polynomials, for example, in training a machine learning classifier using secret shared input data.

Abstract: Embodiments of the present invention generally relate to an interactive sports gaming device and methods of manufacturing and utilizing same. More specifically, embodiments of the present invention relate to an interactive sports gaming device simulating the feel of an authentic sports accessory, capable of providing a cost-effective training device for actual sports play. In one embodiment of the present invention, an interactive sports gaming device comprises a housing for supporting a motion detection controller, the motion detection controller for wirelessly communicating with a video game system, and an authentic handle projecting from a first end of the housing, wherein the interactive sports gaming device simulates the feel of an authentic sports accessory.

Abstract: Embodiments of the present invention generally relate to an interactive sports gaming device and methods of manufacturing and utilizing same. More specifically, embodiments of the present invention relate to an interactive sports gaming device simulating the feel of an authentic sports accessory, capable of providing a cost-effective training device for actual sports play. In one embodiment of the present invention, an interactive sports gaming device comprises a housing for supporting a motion detection controller, the motion detection controller for wirelessly communicating with a video game system, and an authentic handle projecting from a first end of the housing, wherein the interactive sports gaming device simulates the feel of an authentic sports accessory.