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Secure Similarity Queries: Enabling Precision Medicine with Privacy

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Biomedical Data Management and Graph Online Querying (Big-O(Q) 2015, DMAH 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9579))

Abstract

Up till now, most medical treatments are designed for average patients. However, one size doesn’t fit all, treatments that work well for some patients may not work for others. Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in people’s genes, environments, lifestyles, etc. A critical component for precision medicine is to search existing treatments for a new patient by similarity queries. However, this also raises significant concerns about patient privacy, i.e., how such sensitive medical data would be managed and queried while ensuring patient privacy? In this paper, we (1) briefly introduce the background of the precision medicine initiative, (2) review existing secure kNN queries and introduce a new class of secure skyline queries, (3) summarize the challenges and investigate potential techniques for secure skyline queries.

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Acknowledgement

The authors would like to thank the anonymous reviewers for their helpful comments. This research was partially supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM114612 and the Patient-Centered Outcomes Research Institute (PCORI) under award ME-1310-07058.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Emory University, Atlanta, USA

    Jinfei Liu & Li Xiong

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  1. Jinfei Liu
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  2. Li Xiong
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Correspondence to Jinfei Liu .

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Editors and Affiliations

  1. Stony Brook University, Stony Brook, New York, USA

    Fusheng Wang

  2. University of Utah, Salt Lake City, Utah, USA

    Gang Luo

  3. Columbia University, New York, New York, USA

    Chunhua Weng

  4. Nanyang Technological University, Singapore, Singapore

    Arijit Khan

  5. Qatar Computing Research Institute, Doha, Qatar

    Prasenjit Mitra

  6. Google Research, New York, New York, USA

    Cong Yu

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Liu, J., Xiong, L. (2016). Secure Similarity Queries: Enabling Precision Medicine with Privacy. In: Wang, F., Luo, G., Weng, C., Khan, A., Mitra, P., Yu, C. (eds) Biomedical Data Management and Graph Online Querying. Big-O(Q) DMAH 2015 2015. Lecture Notes in Computer Science(), vol 9579. Springer, Cham. https://doi.org/10.1007/978-3-319-41576-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-41576-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41575-8

  • Online ISBN: 978-3-319-41576-5

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