dataesr/scientific-paragraphs-categorization huggingface dataset:
Altogether, we can infer that our Lineage Prediction tool as available on TBminer website is 100% reliable for large lineage assignation based on 15-MIRU and spoligotyping when implemented on human epidemiological datasets in most regions of the world. The only possible region where our tool may provide little information and likely some spurious information could be the Horn of Africa where the diversity deviates from the data we used to implement our tool.
Accuracy of different induction algorithms on the training dataset using 10-fold stratified cross-validation. Naïve Bayes. RF: Random Forest. Vote-5: Vote including the 5 algorithms shown here (from J48 to RF). Vote-10: Vote including the 5 algorithms and their meta-bagging derivatives. *:algorithm used in Lineage Prediction tool on TBminer website. For details on the algorithm, see Material and Methods. Font size underlines performance.
V. Eyring et al. :  Overview of the CMIP6 experimental design and organization -A background volcanic aerosol should be specified that results in radiative forcing matching, as closely as possible, that experienced, on average, during the historical simulation (i. e. 1850-2014 mean). -Models without interactive ozone chemistry should specify the pre-industrial ozone fields from a data set produced from a pre-industrial control simulation that uses 1850 emissions and a mean solar forcing averaged over solar cycles 8-10, representative of the mean mid-19th century solar forcing. -For models with interactive chemistry and/or aerosols, the CMIP6 pre-industrial emissions dataset of reactive gases and aerosol precursors should be used. For models without internally calculated aerosol concentrations, a monthly climatological dataset of aerosol physical and optical properties should be used.
People increasingly have their genomes sequenced and some of them share their genomic data online. They do so for various purposes, including to find relatives and to help advance genomic research. An individual's genome carries very sensitive, private information such as its owner's susceptibility to diseases, which could be used for discrimination. Therefore, genomic databases are often anonymized. However, an individual's genotype is also linked to visible phenotypic traits, such as eye or hair color, which can be used to re-identify users in anonymized public genomic databases, thus raising severe privacy issues. For instance, an adversary can identify a target's genome using known her phenotypic traits and subsequently infer her susceptibility to Alzheimer's disease. In this paper, we quantify, based on various phenotypic traits, the extent of this threat in several scenarios by implementing de-anonymization attacks on a genomic database of OpenSNP users sequenced by 23andMe.
Our experimental results show that the proportion of correct matches reaches 23% with a supervised approach in a database of 50 participants. Our approach outperforms the baseline by a factor of four, in terms of the proportion of correct matches, in most scenarios. We also evaluate the adversary's ability to predict individuals' predisposition to Alzheimer's disease, and we observe that the inference error can be halved compared to the baseline. We also analyze the effect of the number of known phenotypic traits on the success rate of the attack. As progress is made in genomic research, especially for genotype-phenotype associations, the threat presented in this paper will become more serious.
Introduction Due to the decreasing cost of genome sequencing, more and more people have their genotypes sequenced. The most popular direct-to-consumer service provider, 23andMe, 1 has already genotyped more than 800,000 individuals' DNA. This new availability of genomic data is paving the way for revolutionary medical progress. Among other benefits, access to genomic data enables personalized medicine (e. g. , personal drug dosing) and early diagnosis of severe genetic diseases (such as Alzheimer's or Parkinson's). In order to help research progress (typically genome-wide association studies) and benefit from personalized treatment, a significant number of genotyped individuals share their genomic data online (on platforms like OpenSNP 2  [13]  or Personal Genome Project 3 ) or on semi-public databases (e.
For instance, the relationship between SNPs and phenotypes is increasingly used in forensics for reconstructing facial composites from DNA information  [7, 8]. 5 Therefore, if an adversary has access to phenotypic traits (e. g. , visible traits) of an identified individual, he can use known correlations between phenotypic traits and genomic data to identify the genotype of this individual in a genomic database and to infer other sensitive information (such as predispositions to severe diseases) by using the de-anonymized genomic data, as illustrated in Figure  1. The adversary also have access to anonymized genotypes through a collaborative genome-sharing platform (such as the Personal Genome Project) and want to de-anonymize them by relying upon phenotypic information gathered on online social networks (OSNs).
The matching between OSNs and genomic profiles is (even) easier if, for example, the ZIP code is available with the genomic profiles, thus enabling the OSN profiles to be filtered before the matching attack. The adversary might also want to match different online identities, e. g. , OpenSNP profiles that contain genomic data with PatientsLikeMe 6 profiles that contain phenotypic information (mainly health condition, such as diseases). 4 These belong to identity tracing attacks in the categorization proposed by Erlich and Narayanan  [10]. The novelty of the attack presented in this paper lies in the observed data (i. e. , phenotypic traits) and the considered probabilistic relationships (i. e. , genotype-phenotype), as we describe below. 5 Such techniques are used in practice; for instance the police department of Columbia, SC, USA, recently released DNAbased facial composites generated with the Parabon Snapshot™ DNA Phenotyping Service  [31].
6 https://www. patientslikeme. com/ More specifically, we study two de-anonymization attacks: (i) the identification attack, where the adversary wants to identify the genotype (among multiple genotypes) that corresponds to a given phenotype, and (ii) the perfect matching attack, where the adversary wants to match multiple phenotypes to their corresponding genotypes. We rely upon analytical tools for maximizing the matching likelihood in both attacks, and we assume two types of background knowledge: one that makes use of existing genetic knowledge from the association between SNPs and phenotypic traits (unsupervised approach), and another that learns the genomicphenotypic statistical relationships from datasets containing both data types (i. e. , genomic and phenotypic). Our experimental results show, with a database of 80 participants, in the identification attack, a proportion of 13% correct matches in the supervised case, and 5% in the unsupervised case.
These results constitute a significant improvement: they outperform the considered baseline by a factor of eight and three, respectively. When the database size decreases to 10, the attack success increases to around 44% in the unsupervised case, and 52% in the supervised case. We also evaluate the adversary's ability to predict the predisposition to Alzheimer's disease of the database participants. With 10 participants, the average error of the adversary is halved when using the identification attack. In the perfect matching attack, the proportion of correct matches is slightly better than in the identification attack: 16% in the supervised case and 8% in the unsupervised case. For a database of size 10, this proportion increases to 65% and 58%, respectively. With this size, the proportion of correct match is around four times higher than the baseline for both supervised and unsupervised approaches.
We also evaluate the impact of the distinguishability between two individuals on the success of the perfect matching attack. Our results clearly show that the more distinguishable two individuals are, the more likely their genomic (or phenotypic) data will be de-anonymized. This leads us to conclude that the threat on genomic privacy posed by our deanonymization attacks will become even more serious in the near future, when more SNP-trait association information is discovered by genomic researchers, and available to the adversary. Finally, we propose various countermeasures for mitigating the performance of deanonymization attacks against genomic databases. The rest of the paper is organized as follows. In Section 2, we introduce the important concepts used in the paper. In Section 3, we describe the system and adversarial model, including various examples of deanonymization attacks.
System and Adversarial Model The adversary is assumed to have access to two distinct datasets: (i) a set G = {g 1 , g 2 ,. , g n } of genotypes of n different individuals, where g i = (g i,1 ,. , g i,s ) is a vector containing the SNP values of individual i, with g i,j = {0, 1, 2} 7  and (ii) a set P = {p 1 , p 2 ,. , p m } of phenotypes of m individuals, where p i = (p i,1 ,. , p i,t ) is a vector containing the values of phenotypic traits of i. Note that p i,j ∈ P j , where P j is the set of values trait j can take. For instance, if trait j represents eye color, we could have P j = {"brown","blue","green"}. The genomic dataset can be gathered online, on platforms such as 1000 Genomes Project, the Personal Genome Project, OpenSNP, or be leaked to the adversary through a major security breach, e.
g. , of a medical database. The phenotypic traits can be collected online, via online social networks (OSNs) (e. g. , Facebook, PatientsLikeMe), or by having access to medical databases, or even by learning about them in real life. Not all participants have all their s SNP values known, or all t traits accessible. We consider the general problem where some auxiliary information is provided possibly with the phenotypic data (e. g. , name), and some other auxiliary information goes potentially with the genomic data (e. g. , ZIP code). These auxiliary sets are assumed to be disjoint, thus are not used for matching both datasets. Therefore, in order to learn more information about the participants in these separate databases (e. g. , de-anonymize the genomic data), the adversary seeks to link the genomic dataset with the phenotypic dataset by relying only upon the genomic-phenotypic statistical relationships.
This system and adversarial model enable us to represent various attack scenarios. For instance, this model can represent a curious entity (e. g. , an insurer) who wants to identify the genotype of a targeted individual in a database, knowing some of his phenotypic traits (e. g. , gathered on an OSN), in which case m = 1. It can also model a curious entity that has access to a dataset of anonymized genotypes online indicating some risks to certain severe diseases and who wants to know to whom they belong in order to discriminate them (access to insurance, or price of premiums). It can also represent the situation of a curious person (e. g. , an hospital IT staff) who has access to a database with patients' diseases and another database with patients' anonymized genomes, and who wants to de-anonymize these genomes.
It can also be that the adversary has access to an identified genotype on OpenSNP and wants to use it to identify the corresponding user on PatientsLikeMe (where potentially all medical conditions, their evolution and treatments are available). Note that the adversary can use side information such as the individuals' ZIP code or the age to narrow down the set of possible individuals in the genomic database; this would result into, typically, a few hundreds of individuals at most. Another possible attack (not considered in this paper), which relies on the same line of reasoning as the aforementioned attacks, is to directly infer a individual's genotype based on some of her phenotypic traits and on the genotype-phenotype relationships. In this work, we consider two types of background knowledge available to the attacker for linking the genomic and phenotypic datasets.
In the first model, we assume the adversary to have access to a genomic knowledge database that stores information about the relations between genomic and phenotypic data (e. g. , SNPedia 8 ). Such databases typically depict qualitative relationships between SNPs and phenotypic traits. In the second model, we consider a stronger adversary who has access to population statistics about the genomicphenotypic relations. This represents the scenario where the adversary can construct his knowledge from an existing dataset that contains both genomic and phenotypic data about participants. The former model will be referred to as the unsupervised approach, whereas the latter model will be referred to as the supervised approach. Note finally that population allele frequencies of SNPs are publicly accessible, thus are also part of the background knowledge.
De-anonymization Attacks We present here in detail the formalization of two deanonymization attacks. First, assuming the adversary knows the phenotypic traits of a targeted individual, it wants to identify the target's genotype among n other genotypes in a dataset. We refer to this as the identification attack. Second, assuming the adversary has access to one genomic and one phenotypic dataset containing the same n individuals, it wants to match genotypes to their corresponding phenotypes. We refer to this as the perfect matching attack. In the identification attack, the adversary needs to rank the n genotypes it has access to by decreasing value of the phenotype likelihood given each possible genotype. If k ≤ t traits of target x are observed, and if the number of SNPs j available in the n genotypes vary between 1 and s, the adversary chooses the genotype g i that maximizes the following likelihood: P(p x | g i ) = P(p x,1 ,.
Dataset Collection and Processing In order to evaluate the success of the de-anonymization attack presented in the previous section, we collected a dataset of genomic-phenotypic data from OpenSNP in late 2014. OpenSNP is an online platform where users can upload their raw genotype data obtained from direct-to-consumer genetic testing companies such as 23andMe and FTDNA. Together with their genotypes, users can share the values of some of their phenotypic traits. Although the different traits are specified by OpenSNP (e. g. , eye color), the value of the traits are manually specified by the users in free-text. We downloaded a raw dump of OpenSNP genomic-phenotypic data in October 2014. We filtered out bogus data (e. g. , duplicated, corrupted, empty genotype or phenotype) and, for the sake of homogeneity, we focused on the genomic data obtained from 23andMe.
This left us with the data of 818 individual users out of 1137 entries in the raw dump. Note that for some users, the values of some SNPs and phenotypic traits are not known; this is because some SNPs are not tested by 23andMe and because users specify only a subset of the phenotypic traits that appear on OpenSNP. To obtain statistical association data between SNPs and phenotypic traits, which is used to assign the genotype-phenotype compatibility scores {w i,j } to the edges of the bipartite graph, we followed two different approaches: (1) an unsupervised approach in which only qualitative association data, such as (rs7495174: AG, "blue eyes more likely"), is available, typically from knowledge databases populated by experts, and (2) a supervised approach in which the SNP-trait associations are learned from existing annotated datasets in the form of aggregated statistics such as the proportion of individuals with brown eyes and a value CC for SNP rs8028689.
In the unsupervised approach, described in Section 5. 2. 1, we relied on the qualitative association data from SNPedia, based on which we define probabilistic association models for the relevant SNPtrait pairs (according to SNPedia). In the supervised approach, described in Section 5. 2. 2, we computed the association statistics on a subset of the annotated Open-SNP data, but only for the SNP-trait pairs noted as relevant on SNPedia. We also collected from SNPedia general information about the distribution of the SNP values, for the considered SNPs, across the world population. This information is used as prior distribution in the identification attack when a SNP value is not specified (see Formula 3). Based on the list of SNPs tested by 23andMe, the list of traits specified on OpenSNP (and the proportion of users who specified a value for these traits) and the association data available on SNPedia, we narrowed down the sets of SNPs and traits considered in our evaluation.
In the unsupervised case, we obtained a list of 8 phenotypic traits (deriving from 6 high-level Open-SNP traits, e. g. , the "group O", "group AB or B" and "rhesus" traits all deriving from the blood type) with 21 associated SNPs and the form of the sexual chromosomes (i. e. , XX or XY). In the supervised case, as we do not need qualitative association information (which are learned from the data-we only use relevance association information), we considered another 4 traits and 14 additional SNPs. The complete list of the traits and SNPs we considered is given in Table  1. As on OpenSNP the values of the phenotypic traits are provided by the users in free-text, the phenotypic data had consistency issues (e. g. , due to the use of different capitalization schemes) and subjectivity issues. For instance, the variety of eye colors was quite high in our dataset, with more than fifty distinct values, including very precise information such as "green with amber burst and gray outer ring".
In addition, some of the phenotypic values used on OpenSNP differed from those used on SNPedia. Therefore, we manually translated each possible phenotypic value from OpenSNP to one of the values used on SNPedia, thus narrowing down the set of possible values and reducing the subjectivity of the provided information. Figure  3  shows, in the form of pie charts, the distribution of some of the phenotypic values across our final dataset. To build our final dataset, we further screened the users based on the following criteria: At least 75% of the phenotypic traits and SNPs considered in the unsupervised case are specified,  11  the SNPs associated with the susceptibility to Alzheimer's disease are specified (i. e. , rs7412 and rs429358), no two users have the same genomic or phenotypic information across all the considered SNPs and traits.
Experimental Settings and Results We evaluate the different de-anonymization attacks described in Section 4 in various scenarios, both with an unsupervised and a supervised approach for learning the SNP-trait associations. To do so, we implemented the aforementioned attacks in Python; for the graph maximum-weight matching algorithm, we made use of the implementation from the NetworkX 13 library. We quantify the success of the attack with respect to the two following metrics: (1) the proportion of correctly identified individuals and (2) the accuracy of the susceptibility score (wrt. Alzheimer's disease) computed from the genotypes matched to the individual's phenotype (in the identification attack). Note that for the susceptibility metric, we focus on Alzheimer's disease and SNPs rs7412 and rs429358 that significantly affect an individual's chance of having Alzheimer's by the age of 80  [18].
Unsupervised Case Based on the qualitative association information obtained from SNPedia, we define a probabilistic model 12 To build our dataset, we removed only 14 individuals for uniqueness reasons (most of the removed individuals were so because they specified too few SNPs/phenotypes). Keeping such duplicates in our dataset would slightly degrade the performance of the attacks. Note that, as the number of SNPs/phenotypes increases, profiles will become unique. 13 https://networkx. github. io/ for the SNP-trait compatibility scores (representing the conditional probabilities of traits given SNPs). Table  2  (see page  16)  shows sample probabilistic association models. We first distinguish between the associations that are (quasi-)deterministic, specifically those related to the sex and the blood types and those that are nondeterministic (i.
Supervised Case In the supervised case, we build the SNP-trait association models based on the data (we estimate the con-ditional probabilities on the entire dataset, i. e. , the 94 unique individuals, as the dataset is too small to perform a proper cross-validation). For each SNP-trait pair listed in Table  1 , we set the compatibility scores to the proportions observed in our dataset. For instance, in the case "O" vs. "Not O" blood type trait, we compute the proportion of individuals with SNP value CC for the SNP rs505922 who have a blood type "O". By doing so, we build matrices of compatibility scores with the same format as in the unsupervised case. The rest of the identification/perfect matching attack is the same in the unsupervised and supervised cases.
Results We first look at the success of an identification attack that targets a single phenotype in a genomic database composed of multiple genotypes (at most 80, i. e. , the size of our subsets of distinct individuals). This scenario corresponds to the case where an adversary knows that the genotype of a specific individual (whose traits are partially known to the adversary) is present in a given public database; and the adversary tries to identify the genotype of the targeted individual. In this experiment, we assume that all the phenotypic traits of the targeted individual are known to the adversary-the effect of the number of known traits is analyzed in a different experiment described below. We evaluate the success of the adversary for different values of the size of the genomic database, and we show the results, aggregated over all the individuals from the 20 subsets, in Figure  4.
It can be observed that, unsurprisingly, the supervised approach achieves better results than the unsupervised approach, and also that the more genotypes there are, the higher the correct-match ratio between the supervised and unsupervised approach is. Indeed, when n increases, the proportion of correct matches in the super-vised approach is more than twice as high (i. e. , twice as bad, in terms of privacy) as the one in the unsupervised approach. For instance, for a database of 80 genomes, the proportion of correct matches is around 13% in the supervised case vs. 5% in the unsupervised case. In order to gain insight on the performance of the attack on larger databases, we ran experiments with n=210 (this was made possible by lowering the admission threshold to 55% of specified SNPs/phenotypes, to the detriment of the quality of the data); we obtained a proportion of 5.
2% of correct matches for the identification attack in the supervised case. Overall, the performance of the identification attack is relatively high, in absolute values and compared to a simple baseline that picks uniformly at random among the individuals of the same sex: The proportion of correct matches of this baseline is 1. 6%. We chose this baseline as it is the straightforward genomic-oblivious approach: Once the adversary has ruled out the genotypes that do not match the target's sex, it has no further information to discriminate the remaining genotypes. For a database size of 10 (which corresponds, for instance, to a scenario where an adversary tries to identify the genotype of an individual among DNA samples collected in a room), the proportion of correct matches is around 44% in the unsupervised case (52% in the supervised case), whereas the baseline only achieves 13% of correct matches.