Securing Bulk Content Almost for Free

Content providers often consider the costs of security to be greater than the losses they might incur without it; many view "casual piracy" as their main concern. Our goal is to provide a low cost defense against such attacks while maintaining rigorous security guarantees. Our defense is integrated with and leverages fast forward error correcting codes, such as Tornado codes, which are widely used to facilitate reliable delivery of rich content. We tune one such family of codes - while preserving their original desirable properties - to guarantee that none of the original content can b e recovered whenever a key subset of encoded packets is missing. Ultimately we encrypt only these key codewords (only 4% of all transmissions), making the security overhead negligible.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Extracting Location from Contact Traces

Localization is essential feature for many mobile wireless applications. Data collected from applications such as environmental monitoring, package tracking or position tracking has no meaning without knowing the location of this data. Other applications have location information as a building block for example, geographic routing protocols, data dissemination protocols and location-based services such as sensing coverage. Many of the techniques have the trade-off among many features such as deployment of special hardware, level of accuracy and computation power. In this paper, we present an algorithm that extracts location constraints from the connectivity information. Our solution, which does not require any special hardware and a small number of landmark nodes, uses two types of location constraints. The spatial constraints derive the estimated locations observing which nodes are within communication range of each other. The temporal constraints refine the areas, computed by the spatial constraints, using properties of time and space extracted from a contact trace. The intuition of the temporal constraints is to limit the possible locations that a node can be using its previous and future locations. To quantify this intuitive improvement in refine the nodes estimated areas adding temporal information, we performed simulations using synthetic and real contact traces. The results show this improvement and also the difficulties of using real traces.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.