Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Mini-buckets: a general scheme for generating approximations in automated reasoning
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Adaptive diagnosis in distributed systems
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The practical success of broadcast encryption hinges on the ability to (1) revoke the access of compromised keys and (2) determine which keys have been compromised. In this work we focus on the latter, the so-called traitor tracing problem. We present an adaptive tracing algorithm that selects forensic tests according to the information gain criteria. The results of the tests refine an explicit, Bayesian model of our beliefs that certain keys are compromised. In choosing tests based on this criteria, we significantly reduce the number of tests, as compared to the state-of-the-art techniques, required to identify compromised keys. As part of the work we developed an efficient, distributable inference algorithm that is suitable for our application and also give an efficient heuristic for choosing the optimal test.