Dynamic Privacy-Preserving Genomic Susceptibility Testing

Namazi, Mina; Troncoso-Pastoriza, Juan Ramón; Pérez-González, Fernando

doi:10.1145/2909827.2930791

Cited by 6 publications

(13 citation statements)

References 12 publications

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“…We compare the runtime and storage cost of each involved party in our proposed scheme with just one medical unit in the system while the participants leverage aBHE scheme [5] with the existing privacy-preserving protocol of Namazi. et al [10] which allows storage and processing on genomic data via a homomorphic encryption scheme over lattices denoted by BGV [4] for only one medical unit without access policies. We investigate how adding access policies affects the operations in return for gaining more safety for the genomic data of the patients.…”

Section: Discussionmentioning

confidence: 99%

“…We develop a privacy-preserving genomic susceptibility testing method by leveraging an attribute-based homomorphic cryptosystem based on mathematical hardness problems over lattices. Our work relies on a genomic privacy-preserving scheme for susceptibility testing developed by Namazi et al [10] concerning only one medical unit for medical tests. We enhance this scheme to manage accesses of more than one medical unit through attributes and predicates embedded in the cryptosystem while working on genomic data of a patient.…”

Section: Speciallitymentioning

confidence: 99%

“…They applied a secret sharing method and proxy re-encryption to assist the parties which partially encrypt and decrypt the final results using the Paillier [12] encryption scheme. Later on, another privacy-preserving method was proposed in [10] which calculates the susceptibility testing based on a homomorphic encryption scheme over lattices. In this scheme, the trusted party gets the biological sample from a patient, generates the public parameters and keys, and distributes them among the parties.…”

Section: Related Workmentioning

confidence: 99%

“…1 illustrates the mapping between the parties and their attributes. Unfortunately, the existing genomic privacy-preserving methods in the literature are mainly able to perform only one of the following tasks at a time: 1) process the information for storage or running analyses over the encrypted data with a unique medical unit such as [3,10,11]; or 2) protect medical records and regulate the access controls by deploying cryptographic methods [1,13]. Since the defined policies are not dynamic, subsequent refined calculations cannot be supervised automatically by the system.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic attribute-based privacy-preserving genomic susceptibility testing

Namazi

Eryonucu

Ayday

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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Developments in the field of genomic studies have resulted in the current high availability of genomic data which, in turn, raises significant privacy concerns. As DNA information is unique and correlated among family members, it cannot be regarded just as a matter of individual privacy concern. Due to the need for privacyenhancing methods to protect these sensitive pieces of information, cryptographic solutions are deployed and enabled scientists to work on encrypted genomic data. In this paper, we develop an attributebased privacy-preserving susceptibility testing method in which genomic data of patients is outsourced to an untrustworthy platform. We determine the challenges for the computations required to process the outsourced data and access control simultaneously within patient-doctor interactions. We obtain a non-interactive scheme regarding the contribution of the patient which improves the safety of the user data. Moreover, we exceed the computation performance of the susceptibility testing over the encrypted genomic data while we manage attributes and embedded access policies. Also, we guarantee to protect the privacy of individuals in our proposed scheme.

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Section: Discussionmentioning

confidence: 99%

Section: Speciallitymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic attribute-based privacy-preserving genomic susceptibility testing

Namazi

Eryonucu

Ayday

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Self Cite

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“…Homomorphic encryption has also been applied on other steps of the analysis of genomic data, e.g. for disease susceptibility tests [16]. However, these examples still present some limitations: the most CPU intensive task (i.e., the extend step) has to be performed in the private cloud; the need of an efficient and reliable sensitive data classifier; the use of hash algorithms that may be broken before the expiration of the genomic data they protect.…”

Section: Alignment In the Cloudmentioning

confidence: 99%

Cloud-Assisted Read Alignment and Privacy

Fernandes

Decouchant

Couto

et al. 2017

11th International Conference on Practical Applications of Computational Biology &Amp; Bioinformatics

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Thanks to the rapid advances in sequencing technologies, genomic data is now being produced at an unprecedented rate. To adapt to this growth, several algorithms and paradigm shifts have been proposed to increase the throughput of the classical DNA workflow, e.g. by relying on the cloud to perform CPU intensive operations. However, the scientific community raised an alarm due to the possible privacy-related attacks that can be executed on genomic data. In this paper we review the state of the art in cloud-based alignment algorithms that have been developed for performance. We then present several privacy-preserving mechanisms that have been, or could be, used to align reads at an incremental performance cost. We finally argue for the use of risk analysis throughout the DNA workflow, to strike a balance between performance and protection of data.

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