Abstract: This disclosure relates to, among other things, systems and methods for the secure management and distribution of electronic content over broadcast communication channels. Certain embodiments disclosed herein may allow for implementation of a multi-tenant conditional access system whereby a client device may configure itself between multiple broadcast service operators. Robust key generation and management techniques are also described that may use a derived key structure to protect control words used to descramble broadcast content, providing additional measures of security and implementation redundancy.

Abstract: This disclosure relates to, among other things, systems and methods for the secure management and distribution of electronic content over broadcast communication channels. Certain embodiments disclosed herein may allow for implementation of a multi-tenant conditional access system whereby a client device may configure itself between multiple broadcast service operators. Robust key generation and management techniques are also described that may use a derived key structure to protect control words used to descramble broadcast content, providing additional measures of security and implementation redundancy.

Abstract: Example embodiments provide a system and method for providing optimized channel change using probabilistic modeling. A digital receiver detects an occurrence of a channel event. In response to the detected occurrence, the digital receiver accesses a probabilistic causal model from a data storage device, and dynamically learns in real time one or more probabilities based on the detected channel event and the probabilistic causal model. The digital receiver updates the probabilistic causal model at the data storage device with the learned one or more probabilities. A next channel is determined by the digital receiver based on the updated probabilistic causal model.

Abstract: Example embodiments provide a system and method for providing optimized channel change using probabilistic modeling. A digital receiver detects an occurrence of a channel event. In response to the detected occurrence, the digital receiver accesses a probabilistic causal model from a data storage device, and dynamically learns in real time one or more probabilities based on the detected channel event and the probabilistic causal model. The digital receiver updates the probabilistic causal model at the data storage device with the learned one or more probabilities. A next channel is determined by the digital receiver based on the updated probabilistic causal model.