nips nips2007 nips2007-157 nips2007-157-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Michael Kearns, Jinsong Tan, Jennifer Wortman
Abstract: We provide provably privacy-preserving versions of belief propagation, Gibbs sampling, and other local algorithms — distributed multiparty protocols in which each party or vertex learns only its final local value, and absolutely nothing else. 1
[1] C. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
[2] G. Casella and E. George. Explaining the Gibbs sampler. The American Statistician, 46:167–174, 1992.
[3] Y. Dodis, S. Halevi, and T. Rabin. A cryptographic solution to a game theoretic problem. In CRYPTO, pages 112–130, 2000.
[4] S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741, 1984.
[5] A. Gibbs. Bounding convergence time of the Gibbs sampler in Bayesian image restoration. Biometrika, 87:749–766, 2000.
[6] O. Goldreich. Foundations of Cryptography, Volume 2. Cambridge University Press, 2004.
[7] A. Ihler, J. Fisher III, and A. Willsky. Loopy belief propagation: Convergence and effects of message errors. Journal of Machine Learning Research, 6:905–936, 2005.
[8] M. Kearns, M. Littman, and S. Singh. Graphical models for game theory. In Uncertainty in Artificial Intelligence, 2001.
[9] M. Naor and K. Nissim. Communication preserving protocols for secure function evaluation. In ACM Symposium on Theory of Computing, pages 590–599, 2001.
[10] J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
[11] P. Shenoy and G. Shafer. Axioms for probability and belief-function propagation. In Uncertainty in Artificial Intelligence, pages 169–198, 1990.
[12] V. Teague. Selecting correlated random actions. In Financial Cryptography, pages 181–195, 2004.
[13] J. Yedidia, W. Freeman, and Y. Weiss. Understanding belief propagation and its generalizations. In Exploring Artificial Intelligence in the New Millennium. Morgan Kaufmann, 2003. 7 An example would be intractability of recognizing quadratic residues. 8