### Abstract

The problem of publishing privacy-guaranteed data for hypothesis testing is studied using the maximal leakage (ML) as a metric for privacy and the type-II error exponent as the utility metric. The optimal mechanism (random mapping) that maximizes utility for a bounded leakage guarantee is determined for the entire leakage range for binary datasets. For non-binary datasets, approximations in the high privacy and high utility regimes are developed. The results show that, for any desired leakage level, maximizing utility forces the ML privacy mechanism to reveal partial to complete knowledge about a subset of the source alphabet. The results developed on maximizing a convex function over a polytope may also of an independent interest.

Original language | English (US) |
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Title of host publication | 2017 IEEE International Symposium on Information Theory, ISIT 2017 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 779-783 |

Number of pages | 5 |

ISBN (Electronic) | 9781509040964 |

DOIs | |

State | Published - Aug 9 2017 |

Event | 2017 IEEE International Symposium on Information Theory, ISIT 2017 - Aachen, Germany Duration: Jun 25 2017 → Jun 30 2017 |

### Other

Other | 2017 IEEE International Symposium on Information Theory, ISIT 2017 |
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Country | Germany |

City | Aachen |

Period | 6/25/17 → 6/30/17 |

### Fingerprint

### ASJC Scopus subject areas

- Theoretical Computer Science
- Information Systems
- Modeling and Simulation
- Applied Mathematics

### Cite this

*2017 IEEE International Symposium on Information Theory, ISIT 2017*(pp. 779-783). [8006634] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2017.8006634

**Hypothesis testing under maximal leakage privacy constraints.** / Liao, Jiachun; Sankar, Lalitha; Calmon, Flavio P.; Tan, Vincent Y.F.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2017 IEEE International Symposium on Information Theory, ISIT 2017.*, 8006634, Institute of Electrical and Electronics Engineers Inc., pp. 779-783, 2017 IEEE International Symposium on Information Theory, ISIT 2017, Aachen, Germany, 6/25/17. https://doi.org/10.1109/ISIT.2017.8006634

}

TY - GEN

T1 - Hypothesis testing under maximal leakage privacy constraints

AU - Liao, Jiachun

AU - Sankar, Lalitha

AU - Calmon, Flavio P.

AU - Tan, Vincent Y.F.

PY - 2017/8/9

Y1 - 2017/8/9

N2 - The problem of publishing privacy-guaranteed data for hypothesis testing is studied using the maximal leakage (ML) as a metric for privacy and the type-II error exponent as the utility metric. The optimal mechanism (random mapping) that maximizes utility for a bounded leakage guarantee is determined for the entire leakage range for binary datasets. For non-binary datasets, approximations in the high privacy and high utility regimes are developed. The results show that, for any desired leakage level, maximizing utility forces the ML privacy mechanism to reveal partial to complete knowledge about a subset of the source alphabet. The results developed on maximizing a convex function over a polytope may also of an independent interest.

AB - The problem of publishing privacy-guaranteed data for hypothesis testing is studied using the maximal leakage (ML) as a metric for privacy and the type-II error exponent as the utility metric. The optimal mechanism (random mapping) that maximizes utility for a bounded leakage guarantee is determined for the entire leakage range for binary datasets. For non-binary datasets, approximations in the high privacy and high utility regimes are developed. The results show that, for any desired leakage level, maximizing utility forces the ML privacy mechanism to reveal partial to complete knowledge about a subset of the source alphabet. The results developed on maximizing a convex function over a polytope may also of an independent interest.

UR - http://www.scopus.com/inward/record.url?scp=85034055592&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85034055592&partnerID=8YFLogxK

U2 - 10.1109/ISIT.2017.8006634

DO - 10.1109/ISIT.2017.8006634

M3 - Conference contribution

AN - SCOPUS:85034055592

SP - 779

EP - 783

BT - 2017 IEEE International Symposium on Information Theory, ISIT 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -