Abstract: Sensing and control apparatus for a building HVAC system includes interior and boundary sensors, such as cameras and thermal sensors, generating sensor signals conveying occupancy-related features for an area. A controller uses the sensor signals to produce an occupancy estimate and to generate equipment-control signals to cause the HVAC system to supply conditioned air to the area based on the occupancy estimate. The controller includes fusion systems collectively generating the occupancy estimate by corresponding fusion calculations, the fusion systems producing a boundary occupancy-count change based on sensor signals from the boundary sensors, an interior occupancy count based on sensor signals from the interior sensors, and the overall occupancy estimate. Fusion may be of one or multiple types including cross-modality fusion across different sensor types, within-modality fusion across different instances of same-type sensors, and cross-algorithm fusion using different algorithms for the same sensor(s).

Abstract: A communication system including a receiver to receive training data. An input interface to receive input data coupled to a hardware processor and a memory. The hardware processor is configured to initialize the privacy module using the training data. Generate a trained privacy module, by iteratively optimizing an objective function. Wherein for each iteration the objective function is computed by a combination of a distortion of the useful attributes in the transformed data and of a mutual information between the sensitive attributes and the transformed data. Such that the mutual information is estimated by the auxiliary module that maximizes a conditional likelihood of the sensitive attributes given the transformed data. Receive the input data via the input interface. Apply the trained privacy module on the input data to produce an application specific transformed data. A transmitter to transmit the application specific transformed data over a communication channel.

Abstract: Sensing and control apparatus for a building HVAC system includes interior and boundary sensors, such as cameras and thermal sensors, generating sensor signals conveying occupancy-related features for an area. A controller uses the sensor signals to produce an occupancy estimate and to generate equipment-control signals to cause the HVAC system to supply conditioned air to the area based on the occupancy estimate. The controller includes fusion systems collectively generating the occupancy estimate by corresponding fusion calculations, the fusion systems producing a boundary occupancy-count change based on sensor signals from the boundary sensors, an interior occupancy count based on sensor signals from the interior sensors, and the overall occupancy estimate. Fusion may be of one or multiple types including cross-modality fusion across different sensor types, within-modality fusion across different instances of same-type sensors, and cross-algorithm fusion using different algorithms for the same sensor(s).

Abstract: Systems and methods for protecting a privacy of aggregated data from third parties, while preserving analytical usefulness of the aggregated data. A processor performs an offline training stage by, acquiring stored statistical contribution of sensitive devices that corresponds to the aggregated data, as a function of a state of a sensitive device at each time point within a period of time. Performing a real-time stage by determining a state of the sensitive device contributing to the aggregated data at each time point within the period of time. Selecting, from memory, based on the determined state of the sensitive device at each time point, a corresponding statistical contribution of the sensitive device to the aggregated data at each time point. Subtracting the statistical contribution selected for a time point from a value of the aggregated data at the corresponding time point, to produce modified aggregated data, and transmitting to third parties.

Abstract: A communication system including a receiver to receive training data. An input interface to receive input data coupled to a hardware processor and a memory. The hardware processor is configured to initialize the privacy module using the training data. Generate a trained privacy module, by iteratively optimizing an objective function. Wherein for each iteration the objective function is computed by a combination of a distortion of the useful attributes in the transformed data and of a mutual information between the sensitive attributes and the transformed data. Such that the mutual information is estimated by the auxiliary module that maximizes a conditional likelihood of the sensitive attributes given the transformed data. Receive the input data via the input interface. Apply the trained privacy module on the input data to produce an application specific transformed data. A transmitter to transmit the application specific transformed data over a communication channel.

Abstract: Systems and methods for protecting a privacy of aggregated data from third parties, while preserving analytical usefulness of the aggregated data. A processor performs an offline training stage by, acquiring stored statistical contribution of sensitive devices that corresponds to the aggregated data, as a function of a state of a sensitive device at each time point within a period of time. Performing a real-time stage by determining a state of the sensitive device contributing to the aggregated data at each time point within the period of time. Selecting, from memory, based on the determined state of the sensitive device at each time point, a corresponding statistical contribution of the sensitive device to the aggregated data at each time point. Subtracting the statistical contribution selected for a time point from a value of the aggregated data at the corresponding time point, to produce modified aggregated data, and transmitting to third parties.

Abstract: Embodiments of present invention disclose a system and a method for determining a result of a function applied to a first vector and a second vector, wherein the function is a normalized sum-type function. The first vector is stored at a first processor, and the second vector is stored at a second processor. The system and the method determine a joint empirical probability distribution (JEPD) of the first vector and the second vector using a secure multi-party computation. The function is determined as a normalized summation of products of values of the JEPD with corresponding values of the function.

Abstract: Embodiments of present invention disclose a system and a method for determining a result of a function applied to a first vector and a second vector, wherein the function is a normalized sum-type function. The first vector is stored at a first processor, and the second vector is stored at a second processor. The system and the method determine a joint empirical probability distribution (JEPD) of the first vector and the second vector using a secure multi-party computation. The function is determined as a normalized summation of products of values of the JEPD with corresponding values of the function.