A simple method for solving the LQG stochastic control problem using a polynomial approach
Circuits, Systems, and Signal Processing
Analysis and caching of dependencies
Proceedings of the first ACM SIGPLAN international conference on Functional programming
Quantitative information flow as network flow capacity
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Approximation and Randomization for Quantitative Information-Flow Analysis
CSF '10 Proceedings of the 2010 23rd IEEE Computer Security Foundations Symposium
Airavat: security and privacy for MapReduce
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Distance makes the types grow stronger: a calculus for differential privacy
Proceedings of the 15th ACM SIGPLAN international conference on Functional programming
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Language-based information-flow security
IEEE Journal on Selected Areas in Communications
Proceedings of the 2012 ACM conference on Computer and communications security
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We investigate the integration of two approaches to information security: information flow analysis, in which the dependence between secret inputs and public outputs is tracked through a program, and differential privacy, in which a weak dependence between input and output is permitted but provided only through a relatively small set of known differentially private primitives. We find that information flow for differentially private observations is no harder than dependency tracking. Differential privacy's strong guarantees allow for efficient and accurate dynamic tracking of information flow, allowing the use of existing technology to extend and improve the state of the art for the analysis of differentially private computations.