Browsing by Subject "Genomic data"

Now showing 1 - 4 of 4

Open Access
Collusion-secure watermarking for sequential data
(2017-09) Yılmaz, Arif
In this work, we address the liability issues that may arise due to unauthorized sharing of personal data. We consider a scenario in which an individual shares his sequential data (such as genomic data or location patterns) with several service providers (SPs). In such a scenario, if his data is shared with other third parties without his consent, the individual wants to determine the service provider that is responsible for this unauthorized sharing. To provide this functionality, we propose a novel optimization-based watermarking scheme for sharing of sequential data. Thus, in the case of an unauthorized sharing of sensitive data, the proposed scheme can nd the source of the leakage by checking the watermark inside the leaked data. In particular, the proposed schemes guarantees with a high probability that (i) the SP that receives the data cannot understand the watermarked data points, (ii) when more than one SPs aggregate their data, they still cannot determine the watermarked data points, (iii) even if the unauthorized sharing involves only a portion of the original data, the corresponding SP can be kept responsible for the leakage, and (iv) the added watermark is compliant with the nature of the corresponding data. That is, if there are inherent correlations in the data, the added watermark still preserves such correlations. Watermarking typically means changing certain parts of the data, and hence it may have negative e ects on data utility. The proposed scheme also minimizes such utility loss while it provides the aforementioned security guarantees. Furthermore, we conduct a case study of the proposed scheme on genomic data and show the security and utility guarantees of the proposed scheme.
Open Access
Cryptographic solutions for genomic privacy
(Springer, 2016-02) Ayday, Erman
With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists need large collections of human genomes to further increase this speed. Furthermore, individuals are using their genomes to learn about their (genetic) predispositions to diseases, their ancestries, and even their (genetic) compatibilities with potential partners. This trend has also caused the launch of health-related websites and online social networks (OSNs), in which individuals share their genomic data (e.g., OpenSNP or 23andMe). On the other hand, genomic data carries much sensitive information about its owner. By analyzing the DNA of an individual, it is now possible to learn about his disease predispositions (e.g., for Alzheimer’s or Parkinson’s), ancestries, and physical attributes. The threat to genomic privacy is magnified by the fact that a person’s genome is correlated to his family members’ genomes, thus leading to interdependent privacy risks. In this work, focusing on our existing and ongoing work on genomic privacy, we will first highlight one serious threat for genomic privacy. Then, we will present the high level descriptions of our cryptographic solutions to protect the privacy of genomic data. © International Financial Cryptography Association 2016.
Open Access
Inference attacks against kin genomic privacy
(Institute of Electrical and Electronics Engineers Inc., 2017) Ayday, E.; Humbert M.
Genomic data poses serious interdependent risks: your data might also leak information about your family members' data. Methods attackers use to infer genomic information, as well as recent proposals for enhancing genomic privacy, are discussed. © 2003-2012 IEEE.
Open Access
Threats and solutions for genomic data privacy
(Springer, 2015) Ayday, Erman; Hubaux, J. P.; Gkoulalas-Divani, A.; Loukides, G.
With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists need large collections of human genomes to further increase this speed. Furthermore, individuals are using their genomes to learn about their (genetic) predispositions to diseases, their ancestries, and even their (genetic) compatibilities with potential partners. This trend has also caused the launch of health-related websites and online social networks (OSNs), in which individuals share their genomic data (e.g., OpenSNP or 23andMe). On the other hand, genomic data carries much sensitive information about its owner. By analyzing the DNA of an individual, it is now possible to learn about his disease predispositions (e.g., for Alzheimer’s or Parkinson’s), ancestries, and physical attributes. The threat to genomic privacy is magnified by the fact that a person’s genome is correlated to his family members’ genomes, thus leading to interdependent privacy risks. Thus, in this chapter, focusing on our existing and ongoing work on genomic privacy carried out at EPFL/LCA1, we will first highlight the threats for genomic privacy. Then, we will present the high level descriptions of our solutions to protect the privacy of genomic data and we will discuss future research directions. For a description of the research contributions of other research groups, the reader is referred to Chaps. 16 and 17 of the present volume.