Inference Attacks against Kin Genomic Privacy

Ayday, Erman; Humbert, Mathias

doi:10.1109/msp.2017.3681052

Cited by 25 publications

(18 citation statements)

References 30 publications

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“…The IBS-based privacy attacks we describe here add to a growing set of threats to genetic privacy (Homer et al, 2008; Nyholt et al, 2009; Im et al, 2012; Gymrek et al, 2013; Humbert et al, 2015; Shringarpure and Bustamante, 2015; Edge et al, 2017; Ayday and Humbert, 2017; Kim et al, 2018; Erlich et al, 2018). A person’s genotype includes sensitive health information that might be used for discrimination, particularly as our ability to genetically predict traits and disease predispositions will likely improve over the coming years.…”

Section: Discussionmentioning

confidence: 99%

Attacks on genetic privacy via uploads to genealogical databases

Edge

Coop

2019

Preprint

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7Direct-to-consumer (DTC) genetics services are increasingly popular for genetic genealogy, with 8 tens of millions of customers as of 2019. Several DTC genealogy services allow users to upload 9 their own genetic datasets in order to search for genetic relatives. A user and a target person in 10 the database are identified as genetic relatives if the user's uploaded genome shares one or more 11 sufficiently long segments in common with that of the target person-that is, if the two genomes 12 share one or more long regions identical by state (IBS). IBS matches reveal some information 13 about the genotypes of the target person, particularly if the chromosomal locations of IBS matches 14 are shared with the uploader. Here, we describe several methods by which an adversary who 15 wants to learn the genotypes of people in the database can do so by uploading multiple datasets. 16Depending on the methods used for IBS matching and the information about IBS segments 17 returned to the user, substantial information about users' genotypes can be revealed with a few 18 hundred uploaded datasets. For example, using a method we call IBS tiling, we estimate that an 19 adversary who uploads approximately 900 publicly available genomes could recover at least one 20 allele at SNP sites across up to 82% of the genome of a median person of European ancestries. 21In databases that detect IBS segments using unphased genotypes, approximately 100 uploads of 22 falsified datasets can reveal enough genetic information to allow accurate genome-wide imputation 23 of every person in the database. We provide simple-to-implement suggestions that will prevent the 24 exploits we describe and discuss our results in light of recent trends in genetic privacy, including 25 the recent use of uploads to DTC genetic genealogy services by law enforcement. 26(Regalado, 2019). One of the major applications of DTC genetics has been genetic genealogy. 31Customers of companies such as 23andMe and Ancestry, once they are genotyped, can view a list 32 of other customers who are likely to be genetic relatives. These putative relatives' full names are 33 often given, and sometimes contact details are given as well. Such genealogical matching services 34 are of interest to people who want to find distant genetic relatives to extend their family tree, and 35 can be particularly useful to people who otherwise may not have information about their genetic 36 relatives, such as adoptees or the biological children of sperm donors. Several genetic genealogy 37 services-including GEDmatch, MyHeritage, FamilyTreeDNA, and LivingDNA (Table 1)-allow 38 users to upload their own genetic data if they have been genotyped by another company. These 39 entities generally offer some subset of their services at no charge to uploaders, which helps to 40 grow their databases. Upload services have also been used by law enforcement, with the goal of 41 identifying relatives of the source of a crime-scene sample (Erlich et al., 2018; Edge and Coop, 42 2019), prompting discussion abo...

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Section: Discussionmentioning

confidence: 99%

Attacks on genetic privacy via uploads to genealogical databases

Edge

Coop

2019

Preprint

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“…b) The case of genomic data: Genomic privacy is a complex subject whose discussion involves many ethical and balance of control issues and closely ties to that of the privacy of others versus personal freedom. The topic of who are the affected parties and how their consent decisions should be expressed is still under debate both in the media (e.g., [1]) and in the research community (e.g., [8], [36], [62]). There are several options that could be considered; we discuss how their implementation could be done in ConsenShare.…”

Section: Discussion: Limitations and Extensionmentioning

confidence: 99%

“…Notably, the average total bandwidth consumption for Alice on upload is 4.2 ± 4.8MB (roughly twice the original photo size -because she sends the background image twice 7 ), the average total bandwidth consumption for the CMS and for the IMS on download is 2.1 ± 2.4MB and 2.1 ± 2.4MB, respectively (roughly the original photo size); the average total bandwidth consumption for the CMS on upload is 0.4 ± 1.4. 8 The other cases present negligible bandwidth consumption. Note that in a real system, the CMS upload cost could be substantially reduced by returning an even lower version of the background image to Bob.…”

Section: ) Bandwidthmentioning

confidence: 99%

Consensual and Privacy-Preserving Sharing of Multi-Subject and Interdependent Data

Olteanu

Huguenin

Dacosta³

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

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Individuals share increasing amounts of personal data online. This data often involves-or at least has privacy implications for-data subjects other than the individual who shares it (e.g., photos, genomic data) and the data is shared without their consent. A popular example, with dramatic consequences, is revenge pornography. In this paper, we propose ConsenShare, a system for sharing, in a consensual (wrt the data subjects) and privacy-preserving (wrt both service providers and other individuals) way, data involving subjects other than the uploader. We describe a complete design and implementation of ConsenShare for photos, which relies on image processing and cryptographic techniques, as well as on a two-tier architecture (one entity for detecting the data subjects and contacting them; one entity for hosting the data and for collecting consent). We benchmark the performance (CPU and bandwidth) of ConsenShare by using a dataset of 20k photos from Flickr. We also conduct a survey targeted at Facebook users (N = 321). Our results are quite encouraging: The experimental results demonstrate the feasibility of our approach (i.e., acceptable overheads) and the survey results demonstrate potential interest from the users.

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“…For instance, new high throughput DNA sequencing technologies have drastically reduced the price and democratized DNA analysis. Due to the highly sensitive nature of this data, an important research area has emerged to address the quantification of the risk associated with this information and to protect it [8,66]. The widespread adoption of medical IoT has also introduced new security and privacy questions and concerns.…”

Section: Applied Privacy Methods Used In Healthcarementioning

confidence: 99%

Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring

Jourdan

Boutet

Bahí

et al. 2020

ACM Trans. Comput. Healthcare

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The increasing popularity of wearable consumer products can play a significant role in the healthcare sector. The recognition of human activities from IoT is an important building block in this context. While the analysis of the generated datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this article, we propose a framework that relies on machine learning to efficiently recognise the user activity, useful for personal healthcare monitoring, while limiting the risk of users re-identification from biometric patterns characterizing each individual. To achieve that, we show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. We then design a novel protection mechanism processing the raw signal on the user’s smartphone to select relevant features for activity recognition and normalise features sensitive to re-identification. These unlinkable features are then transferred to the application server. We extensively evaluate our framework with reference datasets: Results show an accurate activity recognition (87%) while limiting the re-identification rate (33%). This represents a slight decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.

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Inference Attacks against Kin Genomic Privacy

Cited by 25 publications

References 30 publications

Attacks on genetic privacy via uploads to genealogical databases

Attacks on genetic privacy via uploads to genealogical databases

Consensual and Privacy-Preserving Sharing of Multi-Subject and Interdependent Data

Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring

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