Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations

@article{Samani2015QuantifyingGP,
  title={Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations},
  author={Sahel Shariati Samani and Zhicong Huang and Erman Ayday and Mark Elliot and Jacques Fellay and Jean-Pierre Hubaux and Zolt{\'a}n Kutalik},
  journal={2015 IEEE Security and Privacy Workshops},
  year={2015},
  pages={32-40}
}
As genomic data becomes widely used, the problem of genomic data privacy becomes a hot interdisciplinary research topic among geneticists, bioinformaticians and security and privacy experts. Practical attacks have been identified on genomic data, and thus break the privacy expectations of individuals who contribute their genomic data to medical research, or simply share their data online. Frustrating as it is, the problem could become even worse. Existing genomic privacy breaches rely on low… CONTINUE READING
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