'Redefining Genomic Privacy: Trust and Empowerment' by Yaniv Erlich, James B. Williams, David Glazer, Kenneth Yocum, Nita Farahany, Maynard Olson, Arvind Narayanan, Lincoln D. Stein, Jan A. Witkowski and Robert C. Kain in (2014) 12(11)
PLoS Biology comments
Fulfilling the promise of the genetic revolution requires the analysis of large
datasets containing information from thousands to millions of
participants. However, sharing human genomic data requires protecting
subjects from potential harm. Current models rely on de-identification
techniques in which privacy versus data utility becomes a zero-sum game.
Instead, we propose the use of trust-enabling techniques to create a
solution in which researchers and participants both win. To do so we
introduce three principles that facilitate trust in genetic research and
outline one possible framework built upon those principles. Our hope is
that such trust-centric frameworks provide a sustainable solution that
reconciles genetic privacy with data sharing and facilitates genetic
research.
The authors state -
Genomic
research promises substantial societal benefits, including improving
health care as well as our understanding of human biology, behavior, and
history. To deliver on this promise, the research and medical
communities require the active participation of a large number of human
volunteers as well as the broad dissemination of genetic datasets.
However, there are serious concerns about potential abuses of genomic
information, such as racial discrimination and denial of services
because of genetic predispositions, or the disclosure of intimate
familial relationships such as nonpaternity events. Contemporary
data-management discussions largely frame the value of data versus the
risks to participants as a zero-sum game, in which one player's gain is
another's loss. Instead, this manuscript proposes a trust-based framework that will
allow both participants and researchers to benefit from data sharing.
Current models for protecting participant data in genetic studies focus on
concealing the participants' identities. This focus is codified in the
legal and ethical frameworks that govern research activities in most
countries. Most data protection regimes were designed to allow the free
flow of de-identified data while restricting the flow of personal
information. For instance, both the Health Insurance Portability and
Accountability Act (HIPAA) and the European Union privacy directive
require either explicit subject consent or proof of minimized risk of
re-identification before data dissemination. In Canada, the test for
whether there is a risk of identification involves ascertaining whether
there is a “serious possibility that an individual could be identified
through the use of that information, alone or in combination with other
available information” .
To that end, the research community employs a fragmented system to
enforce privacy that includes institutional review boards (IRBs), ad hoc
data access committees (DACs), and a range of privacy and security
practices such as the HIPAA Safe Harbor .
The current approach of concealing identities while relying on standard
data security controls suffers from several critical shortcomings.
First, standard data security controls are necessary but not sufficient
for genetic data. For instance, access control and encryption can
ensure the security of information at rest in the same fashion as for
other sensitive (e.g., financial) information, protecting against
outsiders or unauthorized users gaining access to data. However, there
is also a need to prevent misuse of data by a “legitimate” data
recipient. Second, recent advances in re-identification attacks,
specifically against genetic information, reduce the utility of
de-identification techniques. Third, de-identification does not provide individuals with control over data — a core element of information privacy.
With the growing limitations of de-identification, the current paradigm is
not sustainable. At best, participants go through a lengthy, cumbersome,
and poorly understood consent process that tries to predict worst-case
future harm. At worst, they receive empty promises of anonymity. Data
custodians must keep maneuvering between the opposite demands for data
utility and privacy, relegating genetic datasets into silos with
arbitrary access rules. Funding agencies waste resources funding studies
whose datasets cannot be reused across and between large patient
communities because of privacy concerns. Finally, well-intentioned
researchers struggle to obtain genetic data from hard to access
resources. These limitations impede serendipitous and innovative
research and degrade a dataset's research value, with published results
often overturned because of small sample sizes.
'Routes for breaching and protecting genetic privacy' by Erlich and Narayanan in (2014) 15(8)
Nat Rev Genetics 570 states
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.
They comment
Genetic datasets are typically published with additional metadata, such as basic demographic details, inclusion and exclusion criteria, pedigree structure, as well as health conditions that are critical to the study and for secondary analysis. These pieces of metadata can be exploited to trace the identity of the unknown genome.
Unrestricted demographic information conveys substantial power for identity tracing. It has been estimated that the combination of date of birth, sex, and 5-digit zip code uniquely identifies more than 60% of US individuals. In addition, there are extensive public resources with broad population coverage and search interfaces that link demographic quasi-identifiers to individuals, including voter registries, public record search engines (such as PeopleFinders.com) and social media. An initial study reported the successful tracing of the medical record of the Governor of Massachusetts using demographic identifiers in hospital discharge information25. Another study reported the identification of 30% of Personal Genome Project (PGP) participants by demographic profiling that included zip code and exact birthdates found in PGP profiles.
Since the inception of the Health Insurance Portability and Accountability Act (HIPAA) Privacy rule, dissemination of demographic identifiers have been the subject of tight regulation in the US health care system. The safe harbor provision requires that the maximal resolution of any date field, such as hospital admissions, will be in years. In addition, the maximal resolution of a geographical subdivision is the first three digits of a zip code (for zip codes of populations of greater than 20,000). Statistical analyses of the census data and empirical health records have found that the Safe Harbor provision provides reasonable immunity against identity tracing assuming that the adversary has access only to demographic identifiers. The combination of sex, age, ethnic group, and state is unique in less than 0.25%of the populations of each of the states.
Pedigree structures are another piece of metadata that are included in many genetic studies. These structures contain rich information, especially when large kinships are available. A systematic study analysed the distribution of 2,500 two-generation family pedigrees that were sampled from obituaries from a US town of 60,000 individuals. Only the number (but not the order) of male and female individuals in each generation was available. Despite this limited information, about 30% of the pedigree structures were unique, demonstrating the large information content that can be obtained from such data.
Another vulnerability of pedigrees is combining demographic quasi-identifiers across records to boost identity tracing despite HIPAA protections. For example, consider a large pedigree that states the age and state of all participants. The age and state of each participant leaks very minimal information, but knowing the ages of all first and second-degree relatives of an individual dramatically reduces the search space. Moreover, once a single individual in a pedigree is identified, it is easy to link between the identities of other relatives and their genetic datasets. The main limitation of identity tracing using pedigree structures alone is their low searchability. Family trees of most individuals are not publicly available, and their analysis requires indexing a large spectrum of genealogical websites. One notable exception is Israel, where the entire population registry was leaked to the web in 2006, allowing the construction of multi-generation family trees of all Israeli citizens
Identity tracing by genealogical triangulation
Genetic genealogy attracts millions of individuals interested in their ancestry or in discovering distant relatives. To that end, the community has developed impressive online platforms to search for genetic matches, which can be exploited by identity tracers. One potential route of identity tracing is surname inference from Y-chromosome data (Figure 2). In most societies, surnames are passed from father to son, creating a transient correlation with specific Y chromosome haplotypes. The adversary can take advantage of the Y chromosome–surname correlation and compare the Y haplotype of the unknown genome to haplotype records in recreational genetic genealogy databases. A close match with a relatively short time to the most common recent ancestor (MRCA) would signal that the unknown genome likely has the same surname as the record in the database.
The power of surname inference stems from exploiting information from distant patrilineal relatives of the unknown’s genome. Empirical analysis estimated that 10–14% of US white male individuals from the middle and upper classes are subject to surname inference based on scanning the two largest Y-chromosome genealogical websites with a built-in search engine35. Individual surnames are relatively rare in the population, and in most cases a single surname is shared by less than 40,000 US male individuals, which is equivalent to 13 bits of information (Box 1). In terms of identification, successful surname recovery is nearly as powerful as finding one’s zip code. Another feature of surname inference is that surnames are highly searchable. From public record search engines to social networks, numerous online resources offer query interfaces that generate a list of individuals with a specific surname. Surname inference has been utilized to breach genetic privacy in the past. Several sperm donor conceived individuals and adoptees successfully used this technique on their own DNA to trace their biological families. In the context of research samples, a recent study reported five successful surname inferences from Illumina datasets of three large families that were part of the 1000 Genomes project, which eventually exposed the identity of nearly fifty research participants.
The main limitation of surname inference is that haplotype matching relies on comparing Y chromosome Short Tandem Repeats (Y-STRs). Currently, most sequencing studies do not routinely report these markers, and the adversary would have to process large-scale raw sequencing files with a specialized tool. Another complication is false identification of surnames and inference of surnames with spelling variants compared to the original surname. Eliminating incorrect surname hits necessitates access to additional quasi-identifiers such as pedigree structure and typically requires a few hours of manual work. Finally, in certain societies, a surname is not a strong identifier and its inference does not provide the same power for re-identification as in the USA. For example, 400 million people in China hold one of the ten common surnames, and the top hundred surnames cover almost 90% of the population43, dramatically reducing the utility of surname inference for re-identification.
An open research question is the utility of non Y chromosome markers for genealogical triangulation. Websites such as Mitosearch.org and GedMatch.com run open searchable databases for matching mitochondrial and autosomal genotypes, respectively. Our expectation is that mitochondrial data will not be very informative for tracing identities. The resolution of mitochondrial searches is low due to the small size of the mitochondrial genome, meaning that a large number of individuals share the same mitochondrial haplotypes. In addition, matrilineal identifiers such as surname or clan are relatively rare in most human societies, complicating the usage of mitochondria haplotype for identity tracing. Autosomal searches on the other hand can be quite powerful. Genetic genealogy companies have started to market services for dense genome-wide arrays that enable the identification of distant relatives (on the order of 3rd to 4th cousins) with fairly sufficient accuracy. These hits would reduce the search space to no more than a few thousand individuals. The main challenge of this approach would be to derive a list of potential people from a genealogical match. As we stated earlier, family trees of most individuals are not publicly available, making such searches a very demanding task that would require indexing a large spectrum of genealogical websites. With the growing interest in genealogy, this technique might be easier in the future and should be taken into consideration.
'Identifying personal genomes by surname inference' by Gymrek, McGuire, Golan, Halperin and Erlich in (2013) 339(6117)
Science 321-4 comments
Sharing sequencing data sets without identifiers has become a common practice in genomics. Here, we report that surnames can be recovered from personal genomes by profiling short tandem repeats on the Y chromosome (Y-STRs) and querying recreational genetic genealogy databases. We show that a combination of a surname with other types of metadata, such as age and state, can be used to triangulate the identity of the target. A key feature of this technique is that it entirely relies on free, publicly accessible Internet resources. We quantitatively analyze the probability of identification for U.S. males. We further demonstrate the feasibility of this technique by tracing back with high probability the identities of multiple participants in public sequencing projects.
They go on to report
Surname inference from personal genomes puts the privacy of current de-identified public data sets at risk. We focused on the male genomes in the collection of Utah Residents with Northern and Western European Ancestry (CEU). The informed consent of these individuals did not definitively guarantee their privacy and stated that future techniques might be able to identify them. To test the ability to trace back the identities of these samples from personal genomes, we processed with lobSTR 32 Illumina genomes of CEU male founders that reside in public repositories of the 1000 Genomes Project and the European Nucleotide Archive that were sequenced with read lengths of at least 76 bp. Most of these genomes were sequenced to a shallow depth of less than 5× and produced sparse Y-STR haplotypes. We selected the 10 genomes that had the most complete Y-STR haplotypes with a range of 34 to 68 markers to attempt surname recovery. Searching the genetic genealogy databases returned top-matching records with Mormon ancestry in 8 of the 10 individuals for whom the top hit had at least 12 comparable markers. Moreover, for four individuals, the top match consisted of multiple records with the same surname, increasing the confidence that the correct surname was retrieved. This potentially high surname recovery rate stems from a combination of the deep interest in genetic genealogy among this population and the large family sizes, which exponentially increases the number of targeted individuals for every person who is tested.
In five surname recovery cases, we fully identified the CEU individuals and their entire families with very high probabilities (Table 1). These five cases belonged to three pedigrees, in two of which the surnames of both the paternal and maternal grandfathers were recovered. Our strategy for tracing back individuals relied on the recovered surnames as well as publicly available Internet resources such as record search engines, obituaries, and genealogical Web sites, and demographic metadata available in the Coriell Cell Repository Web site. The year of birth was inferred by subtracting the ages in Coriell from the year of collecting samples. Each complete pedigree re-identification took 3 to 7 hours by a single person. The identified families matched exactly to the corresponding pedigree descriptions in the Coriell database: The number of children, the birth order of daughters and sons, and the state of residence were identical. All grandparents were alive in 1984, the year that the CEU cell line collection was established. In the two cases of a dual surname recovery from both grandfathers, the surname of the father and the maiden name of the mother matched exactly to the grandfathers’ surnames, substantially increasing the confidence of the recovery. Coriell also lists the ages during sample collection for these two pedigrees, which agreed with the age differences of all tested cases with the identified family members. Using genealogical Web sites, we traced the patrilineal lineage that connects each identified genome through the MRCA to the record originator in the genetic genealogy database (Fig. 3). This analysis revealed that two to seven meiosis events link the CEU genome to the record source. Finally, we calculated that the probability of finding random families in the Utah population with these exact demographic characteristics is less than 1 in 105 to 5 × 109. In total, surname inference breached the privacy of nearly 50 individuals from these three pedigrees.
This study shows that data release, even of a few markers, from one person can spread through deep genealogical ties and lead to the identification of another person who might have no acquaintance with the person who released his genetic data. The propagation of information through shared male lines amplifies the range of identification, allowing ~135,000 records to potentially target several million U.S. males. Another feature of this identification technique is that it entirely relies on free, publicly available resources. It can be completed end-to-end with only computational tools and an Internet connection. The compatibility of our technique with public record search engines makes it much easier to continue identifying other data sets in the same pedigree, including female genomes, once one male target is identified. We envision that the risk of surname inference will grow in the future. Genetic genealogy enthusiasts add thousands of records to these databases every month. In addition, the advent of third-generation sequencing platforms with longer reads will enable even higher coverage of Y-STR markers, further strengthening the ability to link haplotypes and surnames.
