ArticleProceedings of the ACM Workshop on Privacy in the Electronic Society. ACM Workshop on Privacy in the Electronic Society2020
Preserving Genomic Privacy via Selective Sharing.
Article in Proceedings of the ACM Workshop on Privacy in the Electronic Society. ACM Workshop on Privacy in the Electronic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Privacy-Preserving Fingerprinting Against Collusion and Correlation Threats in Genomic Data.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2024Article
- Ensuring Trust in Genomics Research.... IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications : (TPS-ISA ...). IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications · 2023Article
- Assessing transcriptomic reidentification risks using discriminative sequence models.Genome research · 2023Article
- Genomic Data Sharing under Dependent Local Differential Privacy.CODASPY : proceedings of the ... ACM conference on data and application security and privacy. ACM Conference on Data and Application Security & Privacy · 2022Article
- Using game theory to thwart multistage privacy intrusions when sharing data.Science advances · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although genomic data has significant impact and widespread usage in medical research, it puts individuals' privacy in danger, even if they anonymously or partially share their genomic data. To address this problem, we present a framework that is inspired from differential privacy for sharing individuals' genomic data while preserving their privacy. We assume an individual with some sensitive portion on her genome (e.g., mutations or single nucleotide polymorphisms - SNPs that reveal sensitive information about the individual) that she does not want to share. The goals of the individual are to (i) preserve the privacy of her sensitive data (considering the correlations between the sensitive and non-sensitive part), (ii) preserve the privacy of interdependent data (data that belongs to other individuals that is correlated with her data), and (iii) share as much non-sensitive data as possible to maximize utility of data sharing. As opposed to traditional differential privacy-based data sharing schemes, the proposed scheme does not intentionally add noise to data; it is based on selective sharing of data points. We observe that traditional differential privacy concept does not capture sharing data in such a setting, and hence we first introduce a privacy notation,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.