In many signal processing and machine learning applications, datasets containing private information are held at different locations, requiring the development of distributed privacy-preserving algorithms. Tensor and matrix factorizations are key components of many processing pipelines. In the distributed setting, differentially private algorithms suffer because they introduce noise to guarantee privacy. This paper designs new and improved distributed and differentially private algorithms for two popular matrix and tensor factorization methods: principal component analysis (PCA) and orthogonal tensor decomposition (OTD). The new algorithms employ a correlated noise design scheme to alleviate the effects of noise and can achieve the same noise level as the centralized scenario. Experiments on synthetic and real data illustrate the regimes in which the correlated noise allows performance matching with the centralized setting, outperforming previous methods and demonstrating that meaningful utility is possible while guaranteeing differential privacy.

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Building good feature representations and learning hidden source models typically requires large sample sizes. In many applications, however, the size of the sample at an individual data holder may not be sufficient. One such application is neuroimaging analyses for mental health disorders -there are many individual research groups, each with a moderate number of subjects. Pooling such data can enable efficient feature learning, but privacy concerns prevent sharing the underlying data. We propose a model for private feature learning in which the data holders share differentially private views of their respective datasets to enable collaborative learning of a joint feature map. We give an example of such an algorithm for independent component analysis (ICA) -a popular blind source separation algorithm used in neuroimaging analyses. Our algorithm is a differentially private version of the recently proposed distributed joint ICA algorithm. We evaluate the performance of this method on simulated functional magnetic resonance imaging (fMRI) data.

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A new approach for motion-based representation on the basis of optical flow analysis and random sample consensus (RANSAC) method is proposed in this paper. Optical flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene. It is intuitive that an action can be characterized by the frequent movement of the optical flow points or interest points at different areas of the human figure. Additionally, RANSAC, an iterative method to estimate parameters of a mathematical model from a set of observed data which contains inliers and outliers, can be used to filter out any unwanted interested points all around the scene and keep only those which are related to the particular human's motion. By this manner, the area of the human body within the frame is estimated and this rectangular area is segmented into a number of smaller regions or blocks. The percentage of change of interest points in each block from frame to frame is then recorded. Similar procedure is repeated for different persons performing the same action and the corresponding values are averaged for respective blocks. A matrix constructed by this strategy is used as a feature vector for that particular action. Afterwards, for the purpose of recognition using the extracted feature vectors, a distance-based similarity measure and a support vector machine (SVM)-based classification technique have been exploited. From extensive experimentations upon a standard motion database, itis found that the proposed method offers not only a very high degree of accuracy but also computational savings.