Abstract: A privacy-preserving analysis system that provides functionality to analyze disparate data sets (and identify correlations) while making individual re-identification prohibitively difficult (even through repeated analysis). The system creates a large proxy data set by oversampling the underlying data and randomly masking a predictable number of fields in the proxy data sets to create sufficient uncertainty in the analysis results. The system may also use a distributed encryption process, secure communications, and secure multiparty computing to prevent personally-identifying information in remotely-stored underlying data from being determined. In the distributed encryption process, each of a plurality of distributed computing devices may be configured to encrypt personally-identifying information using an identical process (and identical encryption keys).

Abstract: A privacy-preserving analysis system that provides functionality to analyze disparate data sets (and identify correlations) while making individual re-identification prohibitively difficult (even through repeated analysis). The system creates a large proxy data set by oversampling the underlying data and randomly masking a predictable number of fields in the proxy data sets to create sufficient uncertainty in the analysis results. The system may also use a distributed encryption process, secure communications, and secure multiparty computing to prevent personally-identifying information in remotely-stored underlying data from being determined. In the distributed encryption process, each of a plurality of distributed computing devices may be configured to encrypt personally-identifying information using an identical process (and identical encryption keys).

Abstract: Multiple hypothesis tracking is a system which enables an analytic sensor framework to capture sensor data and simultaneously account for many possible instantiations of objects, trajectories and behaviors that may be represented within the captured data. Each data instantiation is represented by a different likelihood of possibility based upon data used to train the recognition module of the analytic sensor framework and/or prior knowledge of an analyst. The data instantiations for objects, trajectories, and behaviors are identified in real time.

Abstract: The identification and tracking of objects from captured sensor data relies upon statistical modeling methods to sift through large data sets and identify items of interest to users of the system. Statistical modeling methods such as Hidden Markov Models in combination with particle analysis and Bayesian statistical analysis produce items of interest, identify them as objects, and present them to users of the system for identification feedback. The integration of a training component based upon the relative cost of sampling sensors for additional parameters, provides a system that can formulate and present policy decisions on what objects should be tracked, leading to an improvement in continuous data collection and tracking of identified objects within the sensor data set.

Abstract: A computerized object tracking method uses data captured from any of a number of sensor suites deployed in an area of interest to identify and track objects of interest within the area covered by the sensors. Objects of interest are uniquely identified utilizing an ellipse-based model and tracked through complex data sets through the use of particle-filtering techniques. The combination of unique object identification and particle-filtering techniques produces the ability to track any of a number of objects of interest through complex scenes, even when the objects of interest are occluded by other objects within the dataset. The tracking action is presented in real-time to a user of the system and accepts direction and requests from the system user.