Routes for breaching and protecting genetic privacy

Journal name:
Nature Reviews Genetics
Year published:
Published online
Corrected online


We are entering an era of ubiquitous genetic information for research, clinical care and personal curiosity. Sharing these data sets is vital for progress in biomedical research. However, a growing concern is the ability to protect the genetic privacy of the data originators. Here, we present an overview of genetic privacy breaching strategies. We outline the principles of each technique, indicate the underlying assumptions, and assess their technological complexity and maturation. We then review potential mitigation methods for privacy-preserving dissemination of sensitive data and highlight different cases that are relevant to genetic applications.

At a glance


  1. An integrative map of genetic privacy breaching techniques.
    Figure 1: An integrative map of genetic privacy breaching techniques.

    The map contrasts different scenarios, such as identifying de-identified genetic data sets, revealing an attribute from genetic data and unmasking of data. It also shows the interdependencies between the techniques and suggests potential routes to exploit further information after the completion of one attack. There are several simplifying assumptions (black circles). In certain scenarios (such as insurance decisions), uncertainty about the target's identity within a small group of people could still be considered a success (assumption 1). For certain privacy harms (such as surveillance), identity tracing can be considered a success and the end point of the process (assumption 2). The complete DNA sequence is not always necessary (assumption 3).

  2. A possible route for identity tracing.
    Figure 2: A possible route for identity tracing.

    The route combines both metadata and surname inference to triangulate the identity of an unknown genome of a person in the United States (represented by the black silhouette). Without any information, there are ~300 million individuals that could match the genome, which is equivalent to 28 bits of entropy. Inferring the sex by inspecting the sex chromosomes reduces the entropy by 1 bit. The adversary then uses the metadata to find the state of residence and the age, which reduces the entropy to 16 bits. Successful surname recovery (for example, using Ysearch) leaves only ~3 bits of entropy. At this point, the adversary uses public record search engines such as PeopleFinders to generate a list of potential individuals; he or she can use social engineering or pedigree structure to triangulate the person (represented by the red silhouette).

Change history

Corrected online 17 June 2014
In this article, an incorrect citation was given in reference 107. The citation should have been: Ayday, E., Raisaro, J. L., McLaren, P. J., Fellay, J. & Hubaux, J.-P. Privacy-preserving computation of disease risk by using genomic, clinical, and environmental data. Proc. USENIX Security Workshop Health Inf. Technol. (2013). This has now been corrected online. The editors apologize for this error.


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Author information


  1. Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, Massachusetts 02142, USA.

    • Yaniv Erlich
  2. Department of Computer Science, Princeton University, 35 Olden Street, Princeton, New Jersey 08540, USA.

    • Arvind Narayanan

Competing interests statement

The authors declare no competing interests.

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  • Yaniv Erlich

    Yaniv Erlich is a fellow at the Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA. He received his Ph.D. from Cold Spring Harbor Laboratory, New York, USA, in 2010 and his B.Sc. from Tel-Aviv University, Israel, in 2006. Before that, he worked in computer security and was responsible for conducting penetration tests on financial institutes and commercial companies. His research involves developing new algorithms for computational human genetics. Yaniv Erlich's homepage.

  • Arvind Narayanan

    Arvind Narayanan is an assistant professor in the Department of Computer Science, and the Center for Information Technology and Policy at Princeton University, New Jersey, USA. He studies information privacy and security. His research has shown that data anonymization is broken in fundamental ways, for which he jointly received the 2008 Privacy Enhancing Technologies Award. His current research interests include building a platform for privacy-preserving data sharing. Arvind Narayanan's homepage.

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    Differential privacy statistic of an association study.

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