论文信息 - Measuring the Loss of Privacy from Statistics

Measuring the Loss of Privacy from Statistics

We present a specialization of quantitative information flow to programs that compute statistics. We provide an approach for estimating the information flows present in such programs based on Monte Carlo simulation and argue that it is more accurate than previous approaches in this domain.

Michael Carl Tschantz | Aditya V. Nori

[1] Dawn Song,et al. Influence: A Quantitative Approach for Data Integrity (CMU-CyLab-08-005) , 2008 .

[2] Stephen McCamant,et al. A simulation-based proof technique for dynamic information flow , 2007, PLAS '07.

[3] Jonathon Shlens,et al. Estimating Entropy Rates with Bayesian Confidence Intervals , 2005, Neural Computation.

[4] Michael R. Clarkson,et al. Belief in information flow , 2005, 18th IEEE Computer Security Foundations Workshop (CSFW'05).

[5] David Clark,et al. A static analysis for quantifying information flow in a simple imperative language , 2007, J. Comput. Secur..

[6] Cynthia Dwork,et al. Differential Privacy , 2006, ICALP.

[7] D. Song,et al. Influence : A Quantitative Approach for Data Integrity , 2008 .