Constructive Privacy for Shared Genetic Data

Abstract

Abstract:The need for the sharing of genetic data, for instance, in genome-wide association studies is incessantly growing. In parallel, serious privacy concerns rise from a multi-party access to genetic information. Several techniques, such as encryption, have been proposed as solutions for the privacy-preserving sharing of genomes. However, existing programming means do not support guarantees for privacy properties and the performance optimization of genetic applications involving shared data. We propose two contributions in this context. First, we present new cloud-based architectures for cloud-based genetic applications that are motivated by the needs of geneticians. Second, we propose a model and implementation for the composition of watermarking with encryption, fragmentation, and client-side computations for the secure and privacy-preserving sharing of genetic data in the cloud.

On Distributed Collaboration for Biomedical Analyses

“…As future work, we plan to extend an existing framework [10] in order to provide full-fledged support for the security and privacy challenge of distributed biomedical analysis. Furthermore, we plan to extend tools that are used by biomedical researchers to implement a comprehensive development method for distributed analyses.…”

“…In the context of GWAS, even aggregate data from each country cannot be shared because of possible identifying attacks [9]. Therefore, GWA studies require ad-hoc security protocols and architectures to be applied at this level i.e., privacy-preserving GWAS as in [10].…”

“…7) Public cloud storage, hybrid cloud system for data processing: The architecture of Fig. 3(g) is that of the solution proposed in [10] intended to secure collaborative analyses on medical and genetic data. In this architecture, medical and genetic data is secured and stored in a public cloud that can additionally handle computations that do not threaten data privacy; otherwise, there is a trusted party e.g., community cloud which handles computations where e.g., data decryption would be needed.…”

“…2) Secure processing environments (SPE): Secure environments for clear data processing can be constructed based on software support for trusted parties [10] or specialized hardware support, such as Intel's Software Guard Extension (SGX), which provides a notion of secure enclaves. SPEs are of particular interest if homomorphic encryption is not efficient enough or, more generally, if processing encrypted data is not applicable.…”

“…There are also formal models for fragmentation such as constraint-based fragmentation [19]: constraints specify which attributes should be physically separated, and the corresponding heuristics return a minimal number of fragments that satisfy the constraints; in practice, this determines the number of non-communicating servers needed to securely deploy the fragmented database. This type of fragmentation has also been integrated into a framework for secure distributed biomedical analyses [10].…”