Method and systems using privacy-preserving analytics for aggregate data

摘要

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.