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Frontiers of Engineering Reports on Leading-Edge Engineering from the 2007 Symposium (2008) / Chapter Skim
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Privacy in a Networked World--Rebecca N. Wright
Pages 5-12

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
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From page 5...
... Historically, people lived in smaller communities, and there was little movement of people from one community to another. People had very little privacy, but social mechanisms helped prevent abuse of information.
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From page 6...
... The issue of confidentiality, or protecting information in transit or in storage from an unauthorized reader, is a well-understood problem in computer science. That is, how does a sender Alice send a message M to an intended receiver Bob in such a way that Bob learns M but an eavesdropper Eve does not, perhaps even if Eve is an active attacker who has some control over the communication network?
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From page 7...
... In the United States there is a large effort to move toward electronic health records in order to improve medical outcomes as well as reduce the exorbitant cost of health care. Unless solutions can be developed that allow medical practitioners' access to the right personal health information at the right time and in the right circumstances, while also ensuring that it cannot be accessed otherwise or used inappropriately even by those who have legitimate access, privacy will remain a barrier to adoption.
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From page 8...
... Cryptographic techniques provide the tools to protect data privacy by allowing exactly the desired information to be shared while concealing everything else about the data. To illustrate how to use cryptographic techniques to design privacy-preserving solutions to enable mining across distributed parties, we describe a privacy-preserving solution for a particular data-mining task: learning Bayesian networks on a dataset divided among two parties who want to carry out data-mining algorithms on their joint data without sharing their data directly.
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From page 9...
... Learning Bayesian networks includes learning the structure and the corresponding parameters. Bayesian networks can be constructed using expert knowledge or derived from a set of data, or by combining those two methods together.
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From page 10...
... . The overall distributed algorithm for learning Bayesian networks is as follows.
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From page 11...
... Pp. 206-215 in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
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Key Terms

  • bayesian networks
  • bayesian network
  • partitioned data
  • vertically partitioned
  • learning bayesian

  • acm sigkdd
  • privacy preserving
  • sigkdd international
  • network structure
  • network learning

  • original score
  • acm press
  • approximate score
  • score comparison
  • score function

  • score algorithm
  • electronic health
  • secure multiparty
  • multiparty computation
  • scalar product

  • parent z
  • comparison algorithm
  • personal information
  • bob execute
  • cryptographic techniques

  • purely technological
  • social mechanisms
  • international workshop
  • party holds
  • health records


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