Quantifying Privacy via Information Density
<|reference_start|>Quantifying Privacy via Information Density: We examine the relationship between privacy metrics that utilize information density to measure information leakage between a private and a disclosed random variable. Firstly, we prove that bounding the information density from above or below in turn implies a lower or upper bound on the information density, respectively. Using this result, we establish new relationships between local information privacy, asymmetric local information privacy, pointwise maximal leakage and local differential privacy. We further provide applications of these relations to privacy mechanism design. Furthermore, we provide statements showing the equivalence between a lower bound on information density and risk-averse adversaries. More specifically, we prove an equivalence between a guessing framework and a cost-function framework that result in the desired lower bound on the information density.<|reference_end|>
@article{grosse2024quantifying,
    title={Quantifying Privacy via Information Density},
    author={Leonhard Grosse, Sara Saeidian, Parastoo Sadeghi, Tobias J.
  Oechtering, Mikael Skoglund},
    journal={arXiv preprint arXiv:2402.12967},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12967},
    primaryClass={cs.IT cs.CR math.IT}
}