### Abstract

In statistical privacy, utility refers to two concepts: information preservation - how much statistical information is retained by a sanitizing algorithm, and usability - how (and with how much difficulty) does one extract this information to build statistical models, answer queries, etc. Some scenarios incentivize a separation between information preservation and usability, so that the data owner first chooses a sanitizing algorithm to maximize a measure of information preservation and, afterward, the data consumers process the sanitized output according to their needs [22, 46]. We analyze a variety of utility measures and show that the average (over possible outputs of the sanitizer) error of Bayesian decision makers forms the unique class of utility measures that satisfy three axioms related to information preservation. The axioms are agnostic to Bayesian concepts such as subjective probabilities and hence strengthen support for Bayesian views in privacy research. In particular, this result connects information preservation to aspects of usability - if the information preservation of a sanitizing algorithm should be measured as the average error of a Bayesian decision maker, shouldn't Bayesian decision theory be a good choice when it comes to using the sanitized outputs for various purposes? We put this idea to the test in the unattributed histogram problem where our decision-theoretic post-processing algorithm empirically outperforms previously proposed approaches.

Original language | English (US) |
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Title of host publication | SIGMOD 2013 - International Conference on Management of Data |

Pages | 677-688 |

Number of pages | 12 |

DOIs | |

State | Published - Jul 29 2013 |

Event | 2013 ACM SIGMOD Conference on Management of Data, SIGMOD 2013 - New York, NY, United States Duration: Jun 22 2013 → Jun 27 2013 |

### Publication series

Name | Proceedings of the ACM SIGMOD International Conference on Management of Data |
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ISSN (Print) | 0730-8078 |

### Other

Other | 2013 ACM SIGMOD Conference on Management of Data, SIGMOD 2013 |
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Country | United States |

City | New York, NY |

Period | 6/22/13 → 6/27/13 |

### All Science Journal Classification (ASJC) codes

- Software
- Information Systems

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## Cite this

*SIGMOD 2013 - International Conference on Management of Data*(pp. 677-688). (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/2463676.2463721