Browsing by Author "Almadhoun, Nour"

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Open Access
Can you really anonymize the donors of genomic data in today’s digital world?
(Springer, 2016-09) Alser, Mohammed; Almadhoun, Nour; Nouri, Azita; Alkan, Can; Ayday, Erman
The rapid progress in genome sequencing technologies leads to availability of high amounts of genomic data. Accelerating the pace of biomedical breakthroughs and discoveries necessitates not only collecting millions of genetic samples but also granting open access to genetic databases. However, one growing concern is the ability to protect the privacy of sensitive information and its owner. In this work, we survey a wide spectrum of cross-layer privacy breaching strategies to human genomic data (using both public genomic databases and other public non-genomic data). We outline the principles and outcomes of each technique, and assess its technological complexity and maturation. We then review potential privacy-preserving countermeasure mechanisms for each threat. © Springer International Publishing Switzerland 2016.
Open Access
Differential privacy under dependent tuples—the case of genomic privacy
(Oxford University Press, 2020-03) Almadhoun, Nour; Ayday, Erman; Ulusoy, Özgür
Motivation: The rapid progress in genome sequencing has led to high availability of genomic data. Studying these data can greatly help answer the key questions about disease associations and our evolution. However, due to growing privacy concerns about the sensitive information of participants, accessing key results and data of genomic studies (such as genome-wide association studies) is restricted to only trusted individuals. On the other hand, paving the way to biomedical breakthroughs and discoveries requires granting open access to genomic datasets. Privacy-preserving mechanisms can be a solution for granting wider access to such data while protecting their owners. In particular, there has been growing interest in applying the concept of differential privacy (DP) while sharing summary statistics about genomic data. DP provides a mathematically rigorous approach to prevent the risk of membership inference while sharing statistical information about a dataset. However, DP does not consider the dependence between tuples in the dataset, which may degrade the privacy guarantees offered by the DP. Results: In this work, focusing on genomic datasets, we show this drawback of the DP and we propose techniques to mitigate it. First, using a real-world genomic dataset, we demonstrate the feasibility of an inference attack on differentially private query results by utilizing the correlations between the entries in the dataset. The results show the scale of vulnerability when we have dependent tuples in the dataset. We show that the adversary can infer sensitive genomic data about a user from the differentially private results of a query by exploiting the correlations between the genomes of family members. Second, we propose a mechanism for privacy-preserving sharing of statistics from genomic datasets to attain privacy guarantees while taking into consideration the dependence between tuples. By evaluating our mechanism on different genomic datasets, we empirically demonstrate that our proposed mechanism can achieve up to 50% better privacy than traditional DP-based solutions. Availability and implementation: https://github.com/nourmadhoun/Differential-privacy-genomic-inference-attack.
Open Access
Inference attacks against differentially private query results from genomic datasets including dependent tuples
(NLM (Medline), 2020) Almadhoun, Nour; Ayday, Erman; Ulusoy, Özgür
Motivation: The rapid decrease in the sequencing technology costs leads to a revolution in medical research and clinical care. Today, researchers have access to large genomic datasets to study associations between variants and complex traits. However, availability of such genomic datasets also results in new privacy concerns about personal information of the participants in genomic studies. Differential privacy (DP) is one of the rigorous privacy concepts, which received widespread interest for sharing summary statistics from genomic datasets while protecting the privacy of participants against inference attacks. However, DP has a known drawback as it does not consider the correlation between dataset tuples. Therefore, privacy guarantees of DP-based mechanisms may degrade if the dataset includes dependent tuples, which is a common situation for genomic datasets due to the inherent correlations between genomes of family members. Results: In this article, using two real-life genomic datasets, we show that exploiting the correlation between the dataset participants results in significant information leak from differentially private results of complex queries. We formulate this as an attribute inference attack and show the privacy loss in minor allele frequency (MAF) and chisquare queries. Our results show that using the results of differentially private MAF queries and utilizing the dependency between tuples, an adversary can reveal up to 50% more sensitive information about the genome of a target (compared to original privacy guarantees of standard DP-based mechanisms), while differentially privacy chi-square queries can reveal up to 40% more sensitive information. Furthermore, we show that the adversary can use the inferred genomic data obtained from the attribute inference attack to infer the membership of a target in another genomic dataset (e.g. associated with a sensitive trait). Using a log-likelihood-ratio test, our results also show that the inference power of the adversary can be significantly high in such an attack even using inferred (and hence partially incorrect) genomes.