Routes for breaching and protecting genetic privacy (1310.3197v1)

Abstract: We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.

• The paper identifies three main genetic privacy attack strategies—Identity Tracing, ADAD, and Completion Techniques—to highlight vulnerabilities in data sharing.
• It evaluates mitigation techniques including access control, data anonymization methods like k-anonymity, and advanced cryptographic solutions for secure analysis.
• The study emphasizes the need for interdisciplinary approaches to balance genetic research utility with ethical and regulatory standards for privacy protection.

Privacy Challenges and Mitigation Strategies in Genetic Data Sharing

The paper "Routes for Breaching and Protecting Genetic Privacy" by Yaniv Erlich and Arvind Narayanan addresses the critical issue of genetic privacy in the era of ubiquitous genetic information. As the availability and sharing of genetic data escalate for research, clinical care, and genealogy, so do the concerns for maintaining the privacy of individuals whose data is being used. This paper provides a comprehensive mapping of potential privacy breaching attacks and lays out various mitigation techniques aimed at privacy-preserving dissemination of genetic data.

Privacy Breaching Techniques

The authors categorize the privacy breaching strategies targeting genetic data into three main types: Identity Tracing, Attribute Disclosure Attacks via DNA (ADAD), and Completion Techniques.

• Identity Tracing focuses on linking an unknown genetic dataset to the identity of a person. The techniques employed, such as surname inference using Y-chromosome data, demographic identifiers, and side-channel leakage, exploit quasi-identifiers or metadata associated with the genetic data.
• Attribute Disclosure Attacks via DNA (ADAD) attempt to connect sensitive attributes with an identified individual's DNA. A common attack involves genotype frequencies or linkage disequilibrium to match genetic data with associated phenotypic characteristics without explicit identifiers.
• Completion Techniques use partial datasets to infer protected genomic areas. High-profile cases, such as uncovering genomic information from sanitized datasets, demonstrate that even masked sections of a genome can reveal sensitive data if attackers possess adequate supporting reference information.

Mitigation Strategies

To counter these threats, Erlich and Narayanan explore several privacy-preserving technologies and categorize them into different methodological approaches:

1. Access Control: Currently the predominant approach adopted by data custodians. Access control involves securing sensitive data in restricted databases, with permissions granted to verified researchers under strict agreements. Although effective to some extent, this approach faces criticism due to insufficient oversight once data is downloaded.
2. Data Anonymization and Aggregation: Techniques like k-anonymity and differential privacy aim to obscure individual identities within datasets. However, the high dimensionality of genetic information often negates the effectiveness of these techniques without severely diminishing the dataset's utility.
3. Cryptographic Solutions: Approaches such as secure multiparty computation and homomorphic encryption introduce sophisticated cryptographic methods to allow data analysis without revealing individual genetic data. Though promising, these techniques frequently entail high computational costs and remain challenging to deploy on a large scale.

Implications and Future Directions

The implications of this work are profound for genetic research, regulatory frameworks, and technological development. Safeguarding genetic privacy not only aligns with ethical norms but also addresses regulatory compliance in jurisdictions like the US and EU. Improved cryptographic techniques could enhance security protocols, potentially reducing the need for stringent access controls in favor of more flexible and resilient privacy-preserving methods. As machine learning and large-scale analytics evolve, integrating robust privacy measures into the design of genetic data analysis frameworks becomes a vital priority.

In conclusion, tackling genetic privacy issues is not solely a technical challenge; it involves balancing scientific transparency with individual privacy rights. Future progress will likely require collaborative efforts across disciplines, combining enhanced computational models with clear regulatory guidelines and informed societal discourse. This collective endeavor promises to unlock the full potential of genetic data research while respecting the foundational tenets of personal privacy.