arXiv Open Access 2018

A Tunable Measure for Information Leakage

Jiachun Liao Oliver Kosut Lalitha Sankar Flavio P. Calmon

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Abstrak

A tunable measure for information leakage called \textit{maximal $α$-leakage} is introduced. This measure quantifies the maximal gain of an adversary in refining a tilted version of its prior belief of any (potentially random) function of a dataset conditioning on a disclosed dataset. The choice of $α$ determines the specific adversarial action ranging from refining a belief for $α=1$ to guessing the best posterior for $α= \infty$, and for these extremal values this measure simplifies to mutual information (MI) and maximal leakage (MaxL), respectively. For all other $α$ this measure is shown to be the Arimoto channel capacity. Several properties of this measure are proven including: (i) quasi-convexity in the mapping between the original and disclosed datasets; (ii) data processing inequalities; and (iii) a composition property.

Topik & Kata Kunci

cs.IT

Penulis (4)

Jiachun Liao

Oliver Kosut

Lalitha Sankar

Flavio P. Calmon

Format Sitasi

APA MLA BibTeX

Liao, J., Kosut, O., Sankar, L., Calmon, F.P. (2018). A Tunable Measure for Information Leakage. https://arxiv.org/abs/1806.03332

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Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓