Introduction
The exchange of genetic data has the potential to significantly advance personalized medicine, precision medicine, and other forms of interventions. Nonetheless, there are privacy concerns because improper use of data could result in privacy violations for both individuals and their biological relatives. Given the exponential growth of genetic data and the increasing availability of this data to researchers, it is critical to comprehend the state of genome privacy today and recognize the obstacles to creating practical privacy-preserving solutions.
The cost of genome sequencing has drastically decreased in recent years thanks to technological advancements, producing an unprecedented volume of genomic data that is essential for numerous scientific applications. These data are being integrated by a number of research projects with the intention of providing the analysis for a broad range of studies. Simultaneously, there has been a notable growth in the business sector's use of genomic data-driven apps, wherein people' own genomic data is gathered to offer health-related services.
What Are the Solutions to Protect Privacy From Genomic Data?
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Access Control: By limiting sensitive data access to authorized users only, access control techniques reduce the exposure of sensitive data. It is the responsibility of qualified users to make sure that data is maintained properly, will not be used to identify data contributors, and may even need them to submit periodic reports.
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Encryption: To convert the original data, or plaintext, into an encoded format, or ciphertext, encryption techniques rely on results from number theory. A unique kind of encryption known as Homomorphic encryption (HE) permits basic primitives like addition and multiplication to be performed directly on the ciphertext. Many privacy-preserving technologies that share data in federated environments and the cloud use HE approaches. Data that has been homomorphically encrypted is still susceptible to privacy assaults.
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Secure Multiparty Computation (SMC): SMC protocols allow a group of participants to work together to complete a task without disclosing personal information. They are based on cryptography. These methods are appropriate for distributed settings since computations can be carried out without the assistance of a reliable third party. For instance, to carry out GWAS and secure statistical test evaluation and genomic sequence comparison.
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K-anonymity: This ensures that each record is concealed in a group and that, for any record, there are at least k-1 records with the same quasi-identifiers (e.g., Zip Code). The original data are changed by suppression and generalization of attribute values (e.g., 3-digit representation for Zipcode) in order to achieve k-anonymity. K-anonymity has been used at the SNP-level and on quasi-identifier characteristics.
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Differential Privacy: By guaranteeing that an adversary who views the results cannot ascertain whether a person took part in a study, differential privacy offers formal and verifiable privacy protection. Randomized procedures (such as output perturbation) are used to achieve privacy. In GWAS investigations, differential privacy has been used, and specializations have lately been taken into consideration in other genomic applications.
What Are the Potential Avenues for Future Study to Further the Development of Privacy-Preserving Methods for Genetic Data?
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Implementing Privacy-Related Solutions:
Developing and disseminating a broad spectrum of useful privacy tools is essential to advancing the usability of privacy techniques. The DTC context makes the lack of workable privacy solutions even more obvious because solutions must fit into the economic model of the organization. However, creating privacy tools will probably present chances to inform people, as well as research organizations and commercial businesses, about the advantages of privacy in sharing genomic data. These instruments may also serve as testing grounds for newly formulated moral and legal standards pertaining to data exchange.
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Assessing Privacy Dangers:
Determining the best privacy technique or policy for genetic data sharing requires an assessment of privacy concerns vs potential advantages. It is difficult to model privacy risk since it might vary depending on the adversary's strength and the information that is available. Given the current state of research, it is plausible to assume that in the near future, publicly available genomic data that do not currently pose privacy problems could.
It is naive for some to accept the stance that an absence of known assaults equals an absence of attacks. It is impractical to share data in any form because of the stance taken by others, according to which only full privacy is acceptable. As a result, the creation of solutions that combine technical risk assessment techniques with suitable legislation (such as data agreements and policies) may aid in identifying specific privacy concerns and direct the creation of fresh approaches to enhancing data usability.
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Technology for Managing the Movement of Data:
People can immediately share their genetic data in the current DTC data-sharing framework in order to obtain health-related services. On the other hand, users frequently have little access to or control over their data. Technological developments in the privacy and security space could shift this paradigm by enabling people to control, monitor, and possibly even make money off of their genetic data. Among these, blockchain technology creates an immutable distributed ledger—a chain of immutable blocks that records data transactions—which has a number of potential advantages for data sharing. A higher level of confidence can be attained by allowing people to monitor the data and providing a complete history of the data's provenance or chain of custody.
- Initiatives for Data Sharing in Privacy:
Data standardization and privacy protection techniques are becoming more and more important, regardless of whether genomic data are exchanged for medicinal research or for other purposes. In order to solve the privacy and data harmonization issues in cooperative research endeavors, a number of projects have been formed. Nevertheless, quality and privacy criteria are not entirely obvious in DTC contexts. In order for people to make educated privacy decisions, initiatives that seek to give them trustworthy, pertinent, and transparent information are necessary.
Conclusion
The development of privacy technologies, which are crucial for expanding the sharing and gathering of genomic data, has made impressive strides in recent years. It is imperative that existing norms and guidelines be progressively improved in tandem with technology advancements. By tackling these technical, legal, and moral issues together, we may enable people to actively participate in science, enhancing the sharing of genomic data and advancing medical research.
