Privacy-Preserving Disease Risk Test Based on Bloom Filters

  • Jun Zhang
  • Linru Zhang
  • Meiqi He
  • Siu-Ming Yiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10631)


Decreasing costs in genome sequencing have been paving the way for personalised medicine. An increasing number of individuals choose to undergo disease risk tests provided by medical units. However, it poses serious privacy threats on both the individuals’ genomic data and the tests’ specifics. Several solutions have been proposed to address the privacy issues, but they all suffer from high storage or communication overhead. In this paper, we put forward a general framework based on bloom filters, reducing the storage cost by 100x. To reduce communication overhead, we create index for encrypted genomic data. We speed up the searching of genomic data by 60x with bloom filter tree, compared to B\(_+\) tree index. Finally, we implement our scheme using the genomic data of a real person. The experimental results show the practicality of our scheme.


Genomic privacy Bloom filters Homomorphic encryption 



This project is partially supported by a collaborative research grant (RGC Project No. CityU C1008-16G) of the Hong Kong Government.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jun Zhang
    • 1
  • Linru Zhang
    • 1
  • Meiqi He
    • 1
  • Siu-Ming Yiu
    • 1
  1. 1.Department of Computer ScienceThe University of Hong KongPok Fu LamHong Kong

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